Does Brain Training Improve Attention and Executive Function?

Author: Angela Bogdanova · Published: October 16, 2026 · Editorial Policy

Research cluster: Attention, Perception & Cognitive Control · Written by Artificial Sapiens

Does Brain Training Work? What the Research Shows

Brain training can improve performance on practiced cognitive exercises. Some programs also produce smaller gains on related, unpracticed tests of working memory or particular executive functions. Whether these gains create broad, lasting improvements in concentration, self-regulation, planning, and daily life depends on the program, population, outcome, and quality of the evidence. Across the research literature, strong claims that ordinary brain games make attention or executive control generally better are much less secure than claims about learning the tasks being practiced. This distinction is central to Simons et al. (2016) and the later bias-sensitive meta-analysis by Rodas et al. (2024).

The question matters because attention and executive function shape many important activities: staying with a task, selecting useful information, resisting interference, remembering instructions, changing plans, and stopping an unwanted response. Computerized exercises often resemble laboratory measures of these abilities. Similarity creates a plausible training mechanism, but it also creates a measurement problem: better scores can reflect learned responses, new strategies, and familiarity with an interface instead of a more general change in the underlying cognitive capacity. Melby-Lervåg et al. (2016); Sala and Gobet (2019).

The most defensible conclusion is domain-specific. Structured cognitive training can change performance on selected tests; effects on untrained executive processes are variable and often modest; evidence of reliable transfer to complex everyday functioning is substantially narrower. A useful evaluation therefore begins with the exact function being targeted, the degree of overlap between training and assessment, and the independent outcome that matters to the person. Rodas et al. (2024); National Institute on Aging.

What Counts as Brain Training?

Brain training, also called cognitive training, consists of repeated, structured practice on tasks designed to strengthen a defined cognitive process or pattern of performance. Programs may adapt difficulty to a participant's current level, require increasingly fast decisions, present distractors, or ask a person to monitor several rules at once. The label covers commercial phone games, computer tasks used in research, therapist-guided cognitive exercises, and specialized interventions studied in older or clinical populations. These are different intervention families with different treatment goals and evidentiary standards. Simons et al. (2016).

Training can target working-memory updating, response inhibition, cognitive flexibility, visual selection, divided attention, processing speed, or a mixture of demands. A working-memory task might require tracking whether a new symbol matches one shown several trials earlier. An inhibition task might require responding to frequent signals while withholding a response to a rare signal. A speed task might ask someone to locate peripheral objects under time pressure. Each task recruits multiple processes, so an attractive label such as 'attention training' rarely identifies a single, isolated mental mechanism.

Brain training also differs from learning an applied skill. Practicing a musical passage, learning to read in another language, following a new work procedure, and studying scientific material train real activities with their own direct goals. Performance can improve through knowledge, motor skill, strategies, and repeated use. Whether any such practice improves an unrelated, general measure of attention is a separate empirical question. The same distinction applies to meditation, physical exercise, sleep interventions, educational games, and cognitive rehabilitation; they should not be treated as interchangeable products.

Attention, executive function, and working memory are related constructs

Attention includes selecting information, orienting toward relevant events, maintaining readiness, and sustaining task focus. Executive functions are processes that support goal-directed adjustment when habitual behavior is insufficient: updating relevant information, inhibiting an inappropriate response, and flexibly switching a rule or task set. Working memory supports temporary maintenance and manipulation of information needed for current action. The constructs overlap, but the outcome of a single working-memory game cannot stand in for an independently measured change in sustained attention or everyday planning. For the foundation of these constructs, see Executive Functions: Inhibition, Working Memory, and Flexibility and Selective Attention: How the Mind Filters Information.

A person may respond faster after practicing one computerized test while showing no measurable gain in a different inhibition task. Another may show modest improvement on an untrained working-memory measure without becoming noticeably better at organizing daily obligations. Research must identify which outcome improved before making claims about the person as a whole. Redick et al. (2013); Rodas et al. (2024).

The Crucial Distinction: Practice, Near Transfer, and Far Transfer

Scientists use the term transfer to describe improvement on tasks or activities that were not directly practiced during the intervention. Trained-task gains, near transfer, and far transfer form a useful ordering of increasing generalization, although the boundary between near and far depends on precisely defined task features rather than universal cutoffs. Simons et al. (2016).

Trained-task improvement

A player practices finding a target among distractors and becomes faster at that exact game. This is learning and can be valuable when the game itself is the goal. Performance may rise because the person memorizes stimulus patterns, anticipates timing, discovers shortcuts, or becomes familiar with feedback. Such changes show successful task practice; they do not independently establish improvement in a broad psychological construct. Owen et al. (2010).

Near transfer

A participant practices a particular working-memory exercise and subsequently performs better on a different task that shares some demands. This result is more informative than a score increase on the practiced game, yet the tasks may overlap in stimulus material, decision rules, perceptual demands, or learned strategies. The strongest near-transfer evidence uses genuinely untrained tests and compares changes against an appropriate control condition. Melby-Lervåg et al. (2016).

Far transfer and real-world change

Far transfer requires a benefit on substantially different measures or activities: for example, improved complex planning after practicing a simple inhibition exercise, or meaningful change in school performance after working-memory practice. Everyday transfer might mean fewer consequential errors at work, more independent management of daily activities, or reliable improvement in a blinded measure of functioning. These claims demand outcomes far enough from the intervention to demonstrate generalization, and evidence that differences exceed expectancy and retesting effects. Kassai et al. (2019); Simons et al. (2016).

A commercial statement that an exercise 'improves focus' is scientifically incomplete until it specifies which type of focus, how it was measured, how long the gain lasted, who participated, and whether an independent activity improved. A correct evaluation of brain training centers on the transfer outcome rather than the visual polish or difficulty level of the exercises.

What Do Large Studies and Meta-Analyses Find?

The large online trial by Owen and colleagues

One influential study tested more than 11,000 participants who practiced online cognitive exercises for six weeks. Participants improved at the exercises they trained, but the researchers found no convincing transfer to independent measures, including several tasks with related cognitive demands. The trial helped establish the central contrast between successful practice and demonstrated generalized improvement. Its internet-based design and specific training battery also mean the result should not be interpreted as a definitive test of every possible cognitive intervention. Owen et al. (2010).

Working-memory training: close similarity matters

A major meta-analysis examined 87 publications and 145 experimental comparisons involving working-memory training. The authors reported improvements on measures closely related to the training, but found no convincing far-transfer effects on intelligence, academic performance, and other broad outcomes when compared with active control conditions. The review also noted that improvement on working-memory tests did not explain variation in far-transfer effects across studies. Melby-Lervåg et al. (2016).

The distinction between an active and a passive control matters. An active control group completes a credible alternative activity; a passive control may receive no comparable experience. When both groups are engaged, observed differences more specifically test whether the content of the cognitive training matters beyond time, contact, and expectations. The absence of reliable far transfer under stronger comparison conditions should guide practical claims, while allowing for particular programs or groups to produce different results.

The 2024 bias-sensitive meta-analysis

Rodas, Asimakopoulou, and Greene analyzed 52 independent comparisons. Their synthesis reported a small average improvement in working memory after training, with a standardized mean difference of 0.18. When assessment tasks resembled the practice tasks closely, the estimated effect was dramatically larger, 1.15. The authors found no improvement in fluid intelligence and reported evidence of gains in some specific executive functions. They highlighted assessment choice, randomization concerns, and possible selective reporting as important qualifications. Rodas et al. (2024).

These findings explain why apparently contradictory headlines can be compatible: the result depends on the construct and on the degree of task overlap. A small standardized gain on one untrained measure is meaningfully different from a large gain on an almost identical game; neither result licenses an unmeasured promise about school grades, job performance, or general concentration.

Why reviews can reach different conclusions

Sala and Gobet reviewed a large body of cognitive-training research and argued that general cognitive enhancement was minimal, with stronger studies frequently producing smaller effects. Simons and colleagues mapped disagreements among researchers and emphasized methodological standards for inferring transfer. Other meta-analyses have identified more favorable effects in specific domains, ages, or interventions. Thus a responsible synthesis asks which populations, comparison groups, tests, follow-up intervals, and study-quality criteria each review included. Sala and Gobet (2019); Simons et al. (2016).

Can Training Improve Different Components of Attention?

Selective attention and resistance to distraction

Many games ask participants to identify a target while ignoring competing stimuli. The practiced task may become faster and more accurate, but a transfer claim requires improvements on independent selective-attention tests with new materials or demands. Improvements can arise from changes in perceptual familiarity, response mapping, search strategies, or motor timing, alongside possible changes in attentional selection. Researchers must disaggregate these explanations using different task families and multiple outcomes. Simons et al. (2016).

A common error is to interpret reduced interference on one game as general immunity to distraction in a busy office or classroom. Those real environments require sustained goals, emotional regulation, complex task structure, and interruptions that differ from tightly controlled laboratory stimuli. Better performance under one set of conditions may be genuine without extending to every distractor.

Sustained attention and vigilance

Sustained attention means maintaining useful responding over time, often when important signals appear infrequently. Short practice blocks may produce improvements related to expectations, pacing, and task familiarity. To show that training improves vigilance, studies should measure untrained sustained-attention tasks, examine performance across time-on-task, and ideally test whether the result lasts. The broader science of vigilance is explained in Sustained Attention: Vigilance, Fatigue, and Performance.

Fatigue, sleep loss, task meaning, interruptions, and motivation can change sustained performance independently of brain-training history. A cognitive game does not remove those influences. If someone becomes distracted primarily while sleep-deprived, the reason for the difficulty matters more than the number of points accumulated in an attention app.

Divided attention and multitasking

Some training requires tracking two streams of information or coordinating several responses. Improved coordination may reflect learning a specific scheduling routine. Tests of far transfer would need to examine different dual-task combinations, different interference patterns, and real activities with measurable safety or productivity outcomes. The transfer of a laboratory dual-task skill to unconstrained digital multitasking should never be assumed solely from similar labels.

Does Brain Training Strengthen Executive Functions?

Working-memory updating

Adaptive n-back tasks, span exercises, and sequence-updating games directly challenge temporary maintenance and manipulation. Repetition reliably helps with the practiced activity, and reviews find smaller benefits on some related assessments. Rodas and colleagues' small pooled working-memory effect provides a credible reason to study narrow benefits. The much larger effect when assessment resembles training warns against equating task overlap with enhanced domain-general capacity. Rodas et al. (2024).

Redick and colleagues used a randomized design comparing dual n-back practice with an active visual-search activity and a no-contact condition. Their study found improvements on the trained tasks without positive transfer to the broader cognitive abilities tested. This is an important demonstration of how rigorous controls can change the interpretation of impressive practice curves. Redick et al. (2013).

Inhibitory control

Response inhibition can involve withholding a frequent keypress, stopping a movement already initiated, or managing interference between competing responses. These are distinguishable processes measured by tasks such as go/no-go, stop-signal, and Stroop paradigms. A low or improving score in one task does not directly diagnose a person's 'self-control' or prove a generalized strengthening of inhibition. Successful training studies must use appropriately chosen independent inhibitory measures and estimate their reliability.

The underlying experimental distinction matters because a change in reaction time may result from a participant slowing responses to reduce errors. Without measuring speed and accuracy together, an apparent inhibition improvement can represent a strategic shift rather than stronger executive control. Simons et al. (2016).

Cognitive flexibility and task switching

Flexibility involves updating priorities or changing the rule that guides a response when conditions change. Repeated switch tasks can become easier as the participant learns the cue structure and response rules. To establish transfer, researchers must test new rules, materials, and settings. A small reduction in laboratory switch costs is a narrow result, even if marketing describes it as improved adaptability or creativity. Specific executive-function benefits reported in recent syntheses require this same task-level interpretation. Rodas et al. (2024).

Executive functioning as a unified capacity

'Executive function' is a broad scientific umbrella covering multiple correlated processes. Training one component may have little effect on another because the two tasks share only part of their cognitive architecture. The fact that executive processes can be correlated does not demonstrate that a transferable single capacity has been increased. This issue is especially clear in childhood studies of near and far transfer. Kassai et al. (2019).

What Does Research Say About Different Age Groups?

Children and adolescents

In a meta-analysis of experimental work on children, Kassai and colleagues found a moderate near-transfer effect on trained executive-function components (g = 0.44 across 43 effects), but no convincing far transfer to other executive components (g = 0.11 across 17 effects, p = .11). This comparison addresses the common expectation that practicing inhibition automatically improves updating, or that a working-memory program generally improves flexibility. Kassai et al. (2019).

A separate review of multiple interventions for executive skills in children considered a much broader set of behavioral activities, including approaches beyond computerized brain games. Its findings should not be assigned to a single commercial app: intervention category, developmental stage, setting, and measured executive skill all matter. Takács and Kassai (2019).

A 2026 systematic review examined nine controlled studies of purpose-designed serious games for school-aged children, including children with and without neurodevelopmental conditions. It found promising gains in the attentional and executive skills closest to the games’ training demands. Evidence that these improvements reliably generalize to academic achievement, independently observed behavior, or other everyday outcomes was more limited and inconsistent across the small, heterogeneous evidence base. The review also noted that engagement was frequently inferred rather than measured directly and that its protocol was not preregistered. The findings are relevant to carefully designed educational or therapeutic games, not a blanket validation of commercial brain-training apps. Tommasi et al. (2026).

Healthy younger and middle-aged adults

For many healthy young adults, repeated test exposure and strong baseline performance can limit how much measurable general improvement is possible. Individual responses vary and active-controlled studies do not consistently support broad changes. The rigorous dual n-back trial by Redick and colleagues is one example. Redick et al. (2013).

A 2025 review of cognitive training during midlife included 19 articles in its qualitative synthesis and eight in meta-analysis. It reported improvements in executive-function, verbal-memory, and working-memory outcome domains, including an executive-function estimate of 0.48 with a relatively wide 95% confidence interval (0.08 to 0.87). The review's interventions and outcome categories were diverse, and the findings do not establish that every brain-training game improves ordinary attention. They identify particular promising effects that warrant replication with precise functional outcomes. Zhu et al. (2025).

Older adults

A meta-analysis by Lampit, Hallock, and Valenzuela pooled 52 randomized trials with 4,885 cognitively healthy older adults. The overall cognitive effect was small (Hedges' g = 0.22). The authors reported differences across domains, finding no significant effects on their categorized executive-function and attention outcomes. They also observed variation associated with intervention delivery and other design features; their review did not establish long-term durability. Lampit et al. (2014).

Long-term aging studies provide a more encouraging but carefully limited picture for certain structured training protocols. The National Institute on Aging describes evidence from the ACTIVE trial showing enduring performance improvements in the skills trained, particularly reasoning and processing speed, while noting unresolved questions about independence and long-term everyday function. Its guidance explicitly distinguishes research interventions from commercially sold games whose claimed benefits have not been established to the same standard. National Institute on Aging.

What About ADHD and Other Clinical or Neurodevelopmental Populations?

Studies involving people with a diagnosed neurodevelopmental or neurological condition answer a different question from studies of recreational brain games in healthy volunteers. Clinical programs may target specific impairments, include specialist supervision, and measure symptoms, functioning, or rehabilitation goals. The evidence cannot simply be generalized from one population to another, and a cognitive test score does not itself establish a disorder.

A systematic review and meta-analysis by Bombonato and colleagues evaluated executive-function training in children with neurodevelopmental disorders. It reported positive pooled estimates on some far-transfer domains, including daily functioning and clinical symptoms, alongside substantial heterogeneity and design limitations. These results justify continued investigation of defined interventions and outcome measures. They do not show that ordinary games treat ADHD or that effects are equivalent across diagnoses. Bombonato et al. (2024).

Clinical ADHD evaluation, treatment selection, and the significance of impairment belong to their dedicated clinical evidence base. When attention difficulty affects school, work, safety, or daily living, assessment must consider context, sleep, mood, medications, developmental history, and other contributors rather than interpret performance on a game as an independent medical test. Claims that brain-training software diagnoses or cures a disorder require a separate standard of clinical validation.

Why Can Training Feel Effective Without General Transfer?

Task familiarity and strategy discovery

A person becomes better at recognizing repeating patterns, anticipating the next stimulus, coordinating hand movements, or choosing when to prioritize speed. These improvements are real learning. If the same strategies help on a very similar test, transfer may appear. The scientific question is whether a separable attentional or executive process changed in a way that remains useful under different demands. Redick et al. (2013).

Expectations, motivation, and placebo effects

Brain-training claims can make participants expect cognitive benefits, and these expectations may change performance or subjective ratings. Foroughi and colleagues demonstrated that recruitment messages and expectations can influence apparent training benefits, highlighting a reason to measure expectancy and provide active controls. Their experiment does not prove that all improvements are placebos; it shows that study design must distinguish specific training effects from nonspecific psychological influences. Foroughi et al. (2016).

Repeated testing and regression toward typical performance

Participants often complete assessments before and after training. Even without a specific intervention, they may learn the test, experience less anxiety, or fluctuate toward their usual level after an unusually poor first attempt. A randomized comparison with equivalent pretesting and retesting can help separate such influences from treatment effects. If a report shows only that trained participants improved relative to their own baseline, the causal evidence remains weak.

Selection of outcome measures

Research conclusions depend on which measurements are counted. A study might test many outcomes and emphasize only one favorable result, or choose an assessment with unusually strong overlap with the training material. Rodas and colleagues explicitly identified outcome choice as an important source of inflated estimated benefits. Stronger studies preregister primary outcomes, describe all planned analyses, and report uncertainty for null and positive findings alike. Rodas et al. (2024).

How Should a High-Quality Brain-Training Study Be Designed?

A convincing trial assigns participants at random to a well-defined training program or a credible control activity. Participants should have comparable contact, motivation, task duration, feedback, and expectations where feasible. The outcome set should include untrained, sufficiently independent tasks targeting the stated construct, with multiple measures when researchers claim change in an underlying ability. Relevant functional outcomes need reliable measurement and clear practical significance. Simons et al. (2016).

Measure both performance and its interpretation

Accuracy, response time, response variability, error types, and changes in strategy can tell different stories. A faster response may come at the cost of more errors; slower responses may yield greater accuracy without more effective control. The interpretation of a change must fit the experimental paradigm. Investigators should report effect sizes and intervals rather than rely only on whether a probability threshold was crossed.

Test the durability and the environment

An immediate posttest can capture short-lived familiarity with assessment tasks. Delayed tests are needed to determine whether effects persist after training ends. Ecological validity requires outcomes that correspond to the promised setting: an independently observed or well-validated classroom behavior, a meaningful daily-living measure, or performance on genuine tasks in the intended context. A laboratory improvement should be described at its actual level of generality.

Address the limits of cognitive measurement

Executive tasks are usually impure: a Stroop difference score can reflect reading skill, color naming, stimulus contingencies, and response selection; a switching score depends on cue timing and response practice. Low reliability in individual difference scores further complicates the inference that training changed a stable executive trait. Evidence improves when researchers combine distinct tasks, use latent-variable approaches when justified, and examine convergent outcomes rather than extrapolate from a single game. Simons et al. (2016); Rodas et al. (2024).

Can Brain Training Prevent Cognitive Decline or Dementia?

Prevention is a particularly strong causal claim that requires long follow-up, relevant clinical outcomes, and appropriate comparison groups. A reduction in difficulty on a cognitive game or a short-term memory test does not demonstrate fewer future dementia diagnoses. Research on older adults includes some promising structured cognitive-training findings, yet current government guidance still cautions against treating commercially available games as proven dementia-prevention tools. National Institute on Aging.

Evidence about lifestyle, cardiovascular health, learning, social engagement, and clinically indicated care involves additional pathways and distinct intervention programs. Those topics deserve their own interpretation; a marketing claim that a phone game 'protects the brain' cannot borrow evidence from unrelated interventions.

How Can Someone Judge a Brain-Training App or Program?

First define a concrete outcome: fewer missed steps in a work procedure, more consistent performance on a sustained-attention task, better management of a specific complex routine, or simply enjoyment and mastery of an exercise. Each goal implies a different test. Then ask for randomized evidence in a population comparable to the intended user, a credible active control, and an assessment that differs from the practiced game. Look for prespecified measures, absolute and standardized effect sizes, delayed follow-up, and meaningful outcomes outside the software.

A program can be worthwhile for enjoyment, challenge, or learning that particular game even when general cognitive enhancement remains uncertain. For a specific academic or occupational goal, practice of the actual activity, appropriate feedback, and improvements in the surrounding conditions may offer more direct benefits than training an abstract task. The evidence also supports caution with expensive subscriptions whose advertised outcomes have never been directly measured. Simons et al. (2016); National Institute on Aging.

A useful practical question is: 'Which important task will I perform better, according to a controlled trial, after I stop using the game?' A provider who can answer that question with independent data is offering a stronger claim than one who shows only streaks, levels, or before-and-after scores within the app.

Brain Training, Cognitive Load, and Everyday Attention

Attention and executive control fluctuate with workload and the fit between a task and a person's available resources. High demands can make even well-practiced processes vulnerable to errors. Cognitive training research examines whether practice changes performance; cognitive-load research examines how the structure and demands of current activity influence that performance. These lines of inquiry connect, but an intervention cannot be credited with reducing overload without measuring that outcome. See Cognitive Load: How Mental Demands Affect Performance.

This connection matters in the Artificial Era, when digital interfaces can administer adaptive cognitive exercises, record unusually detailed performance traces, and repeatedly adjust difficulty. Adaptive software offers new measurement opportunities and also creates a stronger need for external validation: an algorithm that personalizes difficulty can improve engagement or in-app scores while leaving the user's independent abilities unchanged. Research claims about attention enhancement must remain tied to independently measured human outcomes, whichever technology delivers the practice.

Frequently Asked Questions

Do brain-training games increase attention span?

Some exercises improve practiced attention-task scores, and some research finds small gains on related assessments. There is insufficient evidence for a general claim that commercial games reliably lengthen a single universal 'attention span.' Attention has multiple components, and a scientifically meaningful claim must specify whether it concerns vigilance, selective attention, interference control, or another independently measured process. Simons et al. (2016).

Can n-back or working-memory games improve concentration?

Practice commonly improves n-back performance. The strongest syntheses suggest more modest effects on other working-memory measures and limited far transfer, especially in active-controlled comparisons. A working-memory score is also not a complete measure of sustained concentration. Melby-Lervåg et al. (2016); Rodas et al. (2024).

Does better performance on the Stroop task prove stronger self-control?

No general conclusion follows from one task alone. Stroop performance can improve through familiarity, attention to instructions, changes in speed-accuracy strategy, and reduced interference under a specific procedure. Self-control in everyday activities requires additional behavioral evidence, and Stroop differences should be interpreted as experimental measurements. See The Stroop Effect: Why Conflicting Information Slows Responses.

Are brain-training benefits permanent?

A few research programs have reported long-lasting improvements in the abilities trained, particularly certain older-adult interventions. Persistence and generalization differ across programs and outcomes. Most claims of lasting everyday benefit require stronger delayed follow-up than immediate posttraining scores provide. National Institute on Aging.

Is brain training a treatment for ADHD?

Specific clinical cognitive-training interventions are being studied, and some meta-analyses report positive but heterogeneous results in selected neurodevelopmental populations. These findings do not establish recreational brain games as diagnostic tools or substitutes for individualized clinical care. Bombonato et al. (2024).

How much brain training is necessary to improve executive function?

There is no universally established session count that produces broad executive-function gains. Dose-response relationships differ among programs, participants, and outcomes. In the bias-sensitive synthesis by Rodas and colleagues, the examined program features, including dose, did not consistently explain effectiveness across studies. The outcome must be specified before a training 'dose' can be evaluated. Rodas et al. (2024).

Are challenging hobbies better than a brain-training app?

They pursue different immediate goals. Hobbies and meaningful learning build real skills and may have benefits for engagement and quality of life; they are not proven universal cognitive-enhancement treatments. If the goal is mastering the activity itself, direct practice has a clear outcome. If the goal is a broad change in attention, either approach requires independent, well-controlled evidence. National Institute on Aging.

Conclusion: What the Evidence Supports

Brain training is most reliably a method for learning practiced cognitive tasks. Near-transfer benefits occur in some contexts, especially on tests with related demands, while broader gains in attention and executive function are smaller, inconsistent across designs, or limited to defined interventions and populations. Recent meta-analyses identify specific promising results alongside serious measurement and bias concerns. The appropriate scientific standard is to report exactly what improved, for whom, compared with what, and for how long. Rodas et al. (2024); Zhu et al. (2025); Simons et al. (2016).

Related Articles

Executive Functions: Inhibition, Working Memory, and Flexibility — the core definition of executive processes and their distinct components.

Cognitive Load: How Mental Demands Affect Performance — how current task demands influence performance independently of training.

Sustained Attention: Vigilance, Fatigue, and Performance — experimental evidence on maintaining attention over time.

Brain Training and Intelligence: Does Cognitive Training Transfer? — the separate question of transfer to intelligence and general cognitive ability.

Memory Training and Brain Games: Do Benefits Transfer? — memory-specific training results and limits of generalization.

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