Effects of processing speed training on cognitive functions and neural systems

Transcription

1 Rev. Neurosci., Vol. 23(3): , 2012 Copyright by Walter de Gruyter Berlin Boston. DOI /revneuro Effects of processing speed training on cognitive functions and neural systems Hikaru Takeuchi1, * and Ryuta Kawashima Smart Ageing International Research Center, Institute of Development, Aging and Cancer, Tohoku University, 4-1, Seiryo-cho, Aoba-ku, Sendai , Japan 2 Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer, Tohoku University, Sendai , Japan 3 Department of Functional Brain Imaging, Institute of Development, Aging and Cancer, Tohoku University, Sendai , Japan * Corresponding author takehi@idac.tohoku.ac.jp Abstract Processing speed (PS) is an individual cognitive ability that measures the speed with which individuals execute cognitive tasks, particularly elementary cognitive tasks. PS has been proposed to be a key cognitive component, along with working memory, and is psychologically and clinically important. Various types of speed training affect performance of untrained cognitive measures. In this article, we review studies of PS training or training involving speeded tasks and describe the methodologies along with the psychological and neuroimaging findings related to PS training. There are various types of PS (speed) training tasks. Evidence indicates that PS training can enhance performance on untrained speeded tasks. However, the extent of transfer may vary depending on the methodology. A particular type of speed training seems to affect mental health in older adults. Neuroimaging studies of speed training have shown that the effects of speed training on neural mechanisms may vary depending on the training tasks. Adaptive procedures to modulate the difficulties of training tasks based on a subject s performance by modulating the task speed can be applied to various cognitive tasks, and these procedures can perhaps be used to develop training protocols for enhancing various cognitive functions. Keywords: cognitive training; improvement of cognitive function; plasticity; processing speed; processing speed training. Introduction Processing speed (PS) is an individual cognitive ability measuring the speed with which individuals execute cognitive tasks, particularly elementary cognitive tasks (Salthouse, 1996 ). PS along with working memory (WM), which is a limi ted capacity storage system involved in the maintenance and manipulation of information over short periods of time, has been proposed to be a key cognitive component in a wide range of cognitive domains and in psychometric intelligence (Kail and Salthouse, 1994 ; Fry and Hale, 2000 ). The development of intelligence is largely explained by the development of WM capacity, and the development of WM capacity is largely explained by the development of PS (Fry and Hale, 2000 ). PS is also clinically important because impaired PS is associated with normal aging (Wechsler, 1997 ) and neurological and psychiatric disorders such as schizophrenia (Br é bion et al., 1998 ), multiple sclerosis (Rao et al., 1989 ), Parkinson s disease (Lee et al., 2003 ), periventricular white matter hyperintensities (Van den Heuvel et al., 2006 ), type 1 diabetes (Northam et al., 2001 ), late-life depression (Sheline et al., 2006 ), systemic lupus erythematosus (Shucard et al., 2004 ), Alzheimer s disease (Karlinsky et al., 1992 ), and human immunodeficiency virus infections (Ollo et al., 1991 ). Moreover, PS deficits are suggested to mediate deficits of higher-order cognitive functions in these disorders (Br é bion et al., 1998 ; Lee et al., 2003 ; Sheline et al., 2006 ). Previous neuroscientific findings have indicated that the frontal lobe, particularly the dorsolateral prefrontal cortex (DLPFC), brain connectivity, and each brain region involved in information processing are related to PS and rapid cognitive processes. Reduced PS is related to ischemic stroke in the frontal lobe (Leskel ä et al., 1999 ) and lesions in the DLPFC (Stuss et al., 2003 ). In subjects with slower PS, prefrontal regions are recruited more for successful task performance (Rypma et al., 2006 ). Furthermore, subjects with higher PS show higher functional connectivity (FC) between brain regions during task performance (Rypma et al., 2006 ). Higher white matter structural integrity in the parietal and visual cortices is associated with higher PS during a visual task (Tuch et al., 2005 ). Slowed PS seems to be the central cognitive deficit in patients with multiple sclerosis, which is characterized by a white matter deficit (Demaree et al., 1999 ). Finally, rapid cognitive processes of some information are associated with increased recruitment of brain regions that are involved in the processing of particular information. For example, the left insula is associated with linguistic and fluent linguistic output (Flaherty, 2005 ). The superior temporal gyrus is involved in auditory and rapid auditory processing (Poldrack et al., 2001 ). Then, can PS and the associated cognitive functions, disabilities, or neural systems be improved by any means? Various types of PS training have been conducted. As described here, training tasks vary from study to study; some are simple and some complex, some are auditory

2 290 H. Takeuchi and R. Kawashima tasks and some visual tasks. The two biggest studies, the so-called ACTIVE and SKILL studies, utilized a highly complex visual speed training task, which is similar to the task that predicts daily performance and mobility outcomes among older adults. These studies investigated the effects of PS training on various measures in the elderly (Ball et al., 2007 ). In particular, the former study involved several hundred subjects in a PS training group as well as a control group. The latter study involved 100 subjects in a PS training group as well as a control group. These studies revealed the effect of PS training on a task similar to the training as well as driving related measures and mental health. Our recent study utilized various simple PS tasks and investigated the effects of PS training on cognitive functions and neural mechanisms in young subjects. Here we have reviewed these and other studies of PS training and described the methodologies and the psychological and neuroimaging findings of PS training. We have included various types of training studies in which training tasks are sped up in certain ways and referred to them as PS training in this review, although they are not necessarily called PS tasks in traditional psychology. PS training methodologies Most PS training tasks are computerized and performed in an adaptive manner, which means that the difficulties of the tasks are modulated based on the subject s performance. Computerized training has made it easier to adjust the task level automatically. Many studies of PS training have been conducted to date, and the training methodology varies based on the length and the task used in training, the number of training tasks used (1 10 tasks), subject characteristics (e.g., age, cognitive functions), and measures used to investigate the effect of training. The cognitive test batteries noted in this review are summarized in Table 1, and the methods and results of studies of PS training are summarized in Table 2. Training tasks We classified the tasks into four categories. (1) Simple paper and pencil PS tasks. In our previous study, we used simple paper and pencil PS tasks, which were part of the training protocol in our previous study (Takeuchi et al., 2011b ), together with its computerized version. During these tasks, Table 1 Test battery. Test name Benton Visual Retention Test (BVRT) (Lezak, 1995) Block Design (WAIS) (Wechsler, 1997) Choice reaction time (CRT) in the driving simulator measure (CTRd) (Roenker et al., 2003) Controlled Oral Word Association Test (Benton and Hamsher, 1983 ; Lezak, 1995) Digit span (forward/backward) (Edwards et al., 2005) Digit symbol substitution (WAIS) (Wechsler, 1997) Facial Recognition (Benton et al., 1994 ) Finding A s (Ekstrom et al., 1976) Identical Pictures (Ekstrom et al., 1976 ) Information (WAIS) (Wechsler, 1997) Letter comparison task (Salthouse, 1991 ) Minimum Data Set-Home Care (MDS-HC) (Morris et al., 1997 ) Paced Auditory Serial Addition Test (PASAT) (Gronwall, 1977 ) Pattern comparison task (Salthouse, 1991) Raven matrices tests (Raven, 1998) Rey-Osterrieth Complex Figure Test (Rey-O) Copy (Lezak, 1995) Rey-Osterrieth Complex Figure Test (Rey-O) Recall (Lezak, 1995) Road Sign Test (RST) (Ball and Owsley, 2000) Simple reaction time (SRT) in the driving simulator measure (SRTd) Spatial span (forward/backward) (Edwards et al., 2005) Starry Night (Rizzo and Robin, 1996 ; Rizzo et al., 1997 ) Stroop (Edwards et al., 2005) Timed Instrumental Activities of Daily Living (Timed IADL) Test of Non-Verbal Intelligence (TONI) (Mackey et al., 2011) Trail Making Test A (Lezak, 1995) Trail Making Test B, Trail Making Test B-A (Lezak, 1995 ) Useful Field of View (UFOV) (Ball and Owsley, 1993; Ball et al., 1993) Cognitive function Visuospatial working memory, short-term memory Visuospatial problem solving (non-verbal reasoning) PS Verbal fluency, executive functions of mental flexibility and initiation Verbal working memory, short-term memory PS Face recognition without demand on memory and PS PS, visual scanning PS Crystallized intelligence PS Activity of daily living and Instrumental Activities of Daily Living Sustained attention and working memory PS Non-verbal reasoning Perceptual organization and planning Visual memory, perceptual organization and planning Daily visual search skills and visual speed in the driving simulator format PS Visuospatial working memory (Peripheral) Visual sensory function, attention over a spatial array and time Inhibition, executive functions Speed in the conditions that simulate the everyday instrumental activities of daily living Non-verbal reasoning PS, visual scanning Executive function, mental set flexibility Visual PS that predicts crash involvement

3 Processing speed training 291 Table 2 Psychological studies of adaptive PS training. References Training group subjects (number, characteristics, age y.o.) Training duration, total amount Ball et al. (2002) a 712, healthy subjects (mean 74) b Ball et al. (2010) a Edwards et al. (2002) 179, healthy drivers (mean 73) 44, subjects with health problems (mean 74) b 70 min/ses, w, 12 h c 60 m/ses, 10 ses, 6 w, 10 h Edwards et al. (2005) 63, healthy subjects with PS difficulties (mean 75) w, 10 h Edwards et al. (2009a) a Edwards et al. (2009b) Mackey et al. (2011) Roenker et al. (2003) 233, healthy drivers, 61 healthy drivers with a mobility decline risk (total mean 74) 66, healthy drivers with a mobility decline risk (mean 74) 11, healthy subjects (mean 9), 48, healthy drivers with a mobility risk (mean 72) w, 10 h 75 min/ses, 2 ses/w, 8 w, 20 h 2 w, total 4.5 h (on average) Takeuchi et al. (2011b) 23, healthy subjects (mean 22) 4 h/ses, 5 ses, 1 w, 20 h Willis et al. (2006) a 702, healthy subjects (mean 73) b Training tasks Comparison (number, characteristics, age y.o., intervention type) Tests showing training effects Tests not showing training effects (without auditory) (without auditory) (without auditory) Miscellaneous games relating to speed (without auditory) Simple paper and pencil PS tasks, Simple computerized PS tasks, Computerized rapid recognition tasks 704, healthy subjects (mean 74) b, no intervention 409, healthy drivers, (mean 73), no intervention 47, subjects with health problems (mean 74) b, 63, healthy subjects with PS difficulties (mean 76), social and computer contact control 211, healthy drivers,, 63 healthy drivers with a mobility decline risk, social and computer contact control, (total mean 75) 68, healthy drivers with a mobility decline risk (mean 75), social and computer contact control 17, healthy subjects (mean 9), miscellaneous games relating to reasoning f 22, healthy drivers with a mobility risk (mean 72), 4-h simulator driving training UFOV MDS-HC, driving habits, composite scores of (a) three reasoning tasks, (b) three verbal episodic memory tasks, (c) two daily problem-solving tasks, and (d) three daily speed tasks Motor vehicle collisions in real life over 6 years after training UFOV, Timed IADL Road Sign Test, Findings as, Identical Pictures, Letter/Pattern Comparison, Digit symbol, Stroop, Trail Making Test B-A, (Controlled Oral Word Association d ), BVRT, Rey-O Recall, Digit span, Facial Recognition, Information, Block Design UFOV, Timed IADL Road Sign Test, Letter comparison, Likeliness to cease driving over 3 years after training Driving exposure, driving space, and driving difficulty e over 3 years Coding, cross out (both cognitive speed measures) UFOV, CRTd, dangerous driving maneuvers during driving evaluation 21, healthy subjects, (mean 21) Simple PS measures, Intelligence test with speeded tasks, Complex arithmetic task 698, healthy subjects (mean 74) b, no intervention Pattern comparison, Digit symbol substitution, Stroop, Trail Making Test A, Trail Making Test B, Digit span, Spatial span f (TONI d ), Digit span forward, Digit span backward, (Spatial span forward d ), Spatial span backward SRTd, other driving simulator measures Non-verbal reasoning measures, Digit span, Visuospatial WM task, Stroop measures, a creativity test, Simple arithmetic task UFOV at 5 years, IADL difficulty score from MDS-HC at 5 years, composite scores of (a) three reasoning tasks, (b) three verbal episodic memory tasks, (c) two daily problem-solving tasks, and (d) three everyday speed tasks, all at 5 years

4 292 H. Takeuchi and R. Kawashima Table 2 (Continued) References Training group subjects (number, characteristics, age y.o.) et al. (2009a) a et al. (2009b) a et al. (2009b) a et al. (2009c) a et al. (2006b) a et al. (2006a) a et al. (2010) a Vance et al. (2007) Wadley et al. (2006) 452, healthy subjects (mean 75) 407, healthy subjects without suspected clinical depression (mean 74) b 110, subjects with suspected clinical depression (mean 74) b 518, healthy subjects (mean 73) 543, healthy subjects (mean 73) 448, healthy subjects (mean 73) 458, healthy subjects (mean 74) 82, healthy subjects with PS difficulties (mean 75) g, healthy subjects with deficient PS (mean 75) b Training duration, total amount w, 10 h total 10 ses, 12 w on average, 10 h w, 10 h Training tasks Comparison (number, characteristics, age y.o., intervention type) (without auditory) (without auditory) 462, healthy subjects, (mean 76), 407, healthy subjects without suspected clinical depression, 104, subjects with suspected clinical depression (mean 74) b, 512, healthy subjects, (mean 73), 531, healthy subjects (mean 74), 456, healthy subjects (mean 74), 431, healthy subjects, (mean 74), 77, healthy subjects with PS difficulties (mean 76), social and computer contact control healthy subjects with deficient PS (mean 75) b, (n = 27), social and computer contact (n = 37) Tests showing training effects Predicted medical expenditures at 1 year Likeliness to develop suspected clinical depression at 1 year Depressive symptoms Health-related quality of life at 2 years Health-related quality of life at 5 years Self-rated health at 2, 3, and 5 years UFOV, Starry Night, Rey-O Copy Tests not showing training effects Recovery from suspected clinical depression Trail Making Test B, Road Sign Test, Benton Visual Retention test, Rey-O recall, PASAT UFOV Road Sign Test, Trail Making Test B, BVRT Studies in the table are limited to published studies. Furthermore, PS studies that involved too-specific training tasks are introduced in the manuscript but not in this table. w, week; d, day; h, hour; m, minute; ses, session, y.o., years old. a These studies are part of the ACTIVE study. b Mean age of all subjects (intervention groups + control group). c Some of these subjects completed follow-up training sessions. d Superior improvements in performance were observed in the control groups. e The effects of training on these measures were not directly compared between the PS training and control groups in this study. f In this study, the reasoning training group acted as the control group for the PS measure, and the PS training group acted as a control group for the reasoning measure. g In total, 39 subjects were assigned to the group that underwent PS training in the laboratory and 61 subjects were assigned to the group that underwent PS training at home.

5 Processing speed training 293 subjects had to perform simple PS tasks within a limited time as much as possible and with utmost accuracy as in cognitive PS tests, and the procedure was repeated. In the paper and pencil type tasks, difficulties of the tasks cannot be modulated based on the subject s performance through the use of computers, which are known to make the training effective (Klingberg et al., 2002 ). However, subjects can increase cognitive load to a maximum by way of trying to solve the task as fast as possible. One example in our previous study was similar to the visual number matching task (Woodcock, 1997 ). In this task, rows of six double-digit numbers were printed on a paper with two identical numbers in each row (e.g., ). Subjects were instructed to circle either of the identical digits in each row. Furthermore, we have conducted studies of training that involves solving an arithmetic problem as quickly as possible in a limited time, together with other training tasks, although they are not necessarily called PS training (Nouchi et al., 2012 ; H. Takeuchi et al., submitted for publication ). Training in both of these studies led to improved performance on untrained PS tasks, and the arithmetic problem training task may have had an impact on the PS tasks as well. (2) Simple computerized PS tasks. In our previous study, we used simple computerized PS tasks, which were part of the training protocol in our previous study (Takeuchi et al., 2011b ). One type of this task is the modified version of the 0-back task (Takeuchi et al., 2012a ). In these tasks, each stimulus corresponded to each button on the keyboard. For all of these tasks, a particular type of stimulus was presented successively. In each trial, subjects had to push a corresponding button in response to a certain stimulus before the presentation of the next stimuli. Other tasks include computerized versions of PS tasks that were developed as paper and pencil tasks, such as the number comparison task (Earles and Kersten, 1999 ). In this task, a pair of number strings containing three single-digit numbers (0 9) was presented visually and subjects had to decide whether the strings were the same when reordered. If the strings were the same, the subjects pushed a particular button. If they were not the same, the subjects had to push another button. The stimulus presentation rate during these tasks was modulated based on the subject s performance. (3) Computerized rapid recognition tasks. In our previous study, we used computerized rapid recognition tasks, which were part of the training protocol in our previous study (Takeuchi et al., 2011b ). During these tasks, a particular stimulus (e.g., single-digit numbers from 1 to 4) was rapidly presented in succession. Subjects had to push a prespecified button within 1 s after the same stimulus was presented three times in a row. The stimulus presentation rate was modulated based on the subject s performance. (4) Miscellaneous games. Mackey et al. (2011) used a cognitive speed training program consisting of miscellaneous games for children who were the study subjects. The program incorporated a mix of commercially available computerized and non-computerized games, as well as a mix of games that were played individually or in small groups. Games selected for speed training involved rapid visual processing and rapid motor response. This PS training led to significant improvements in standardized PS measures. We were sure whether these improvements corresponded to near transfer or far transfer because there were many training tasks and because details of the tasks were not described. (5) Choice reaction time tasks. Some studies included choice reaction time tasks together with other training tasks without adaptive procedures to modulate difficulty. In choice reaction time tasks, stimuli were presented randomly, and subjects had to push buttons that corresponded to the presented stimuli as fast as possible (Klingberg et al., 2002 ; Schmiedek et al., 2010 ). (6) s [Brief exposure visuospatial divided + non-divided attention task (with/without auditory components)]. The ACTIVE study and some other relevant studies used this computerized task (Jobe et al., 2001 ). According to Jobe et al. (2001), this task focuses on improving visual search speed and the ability to perform one or more attentional tasks quickly. Difficulty was modulated by reducing the stimulus duration and by making the rules of the tasks progressively more complex. In the simplest task (task 1), participants were asked to identify objects at increasingly brief exposures. Once this ability was mastered in the shortest possible stimulus duration, participants proceeded to task 2. In task 2, participants were asked to divide their attention between two tasks: stimulus identification in the center of a large computer monitor and localization of another target presented somewhere in their peripheral vision. Task difficulty was increased by either decreasing the stimulus duration, expanding the area within which targets could be localized, or increasing the difficulty of the central identification task. Once this task was mastered for the most difficult condition, subjects proceeded to task 3. Task 3 added visual distractors to the stimulus display. Task 4 was the most difficult training condition. In task 4, task demand was increased even further by superimposing an auditory identification component over the visual tasks. Thus, these tasks were complex and included components of identifying stimuli during brief exposures, dividing attention (within modalities and across modalities), ignoring attention distractors, and increasing visual areas. Some studies (Edwards et al., 2002 ) used tasks 1 3 (without the auditory component), whereas other studies (Jobe et al., 2001 ) used tasks 1 4 (with the auditory component in task 4). Tasks 1 3 are very similar to the useful field of view (UFOV) task (Edwards et al., 2002 ), which is a good predictor of daily performance and mobility outcomes among older adults (Ball and Owsley, 1993 ; Ball et al., 1993 ). (7) Speed training protocols that focus on specific modalities. Some training studies focused on improving PS of certain types of modalities. For example, Irausquin et al. (2005) developed training aimed at improving the speed of word identification and comprehension. One of the tasks in this training is the flash words task. During this task, the computer presented a spoken word, after which five written words were flashed consecutively in the center of the screen. The words differed only in one grapheme and were flashed with a particular interstimulus interval. Subjects had to click on the screen button below the frame as quickly as possible when they saw the heard word. The interstimulus interval was modulated based on the subject s performance.

6 294 H. Takeuchi and R. Kawashima Factors that affect training In studies of WM training, training involving teaching strategies for problem solving failed to cause a transfer effect. Continuous modulation of the difficulties of training tasks based on the subject s performance in an adaptive manner is important for eliciting the transfer effects of training (Klingberg, 2010 ; Takeuchi et al., 2010b ). This type of adaptive training also enhances sensory discrimination and induces cortical plasticity in sensory and motor cortices (Tallal et al., 1996 ; Buonomano and Merzenich, 1998 ). Thus, an adaptive procedure instead of a teaching strategy may be important for PS training to have its effect on measures other than the tasks similar to trained tasks. In a series of studies that used PS training of visuospatial divided s, there were three groups other than the PS training group, namely a reasoning training group, a memory training group, and a group. Adaptive procedures and not teaching strategies were used in the PS training group, whereas teaching strategies and not modulation of the task based on a subject s performance was followed in the memory and reasoning training group. In these studies, PS training led to improved health-related quality of life, predicted medical expenditures, self-rated health, and protection against depression ( et al., 2006a,b, 2009b,c, 2010 ), whereas the effects of reasoning and memory training on these measures were less clear or not substantially different from those of. These differences in effects may be partly attributable to differences in usage of adaptive procedures and teaching strategies among training groups ( et al., 2010 ). PS training conducted at home has effects comparable to the effects of training conducted at the laboratory by a trainer (Wadley et al., 2006 ). This may help computerized training. Although the effects of the following factors on training outcomes have not been specifically examined in studies of PS training, factors such as motivation, arousal, performance feedback, variability in the training tasks, and larger duration of training may increase the effects of training or efficiency of learning (Green and Bavelier, 2008 ; Takeuchi et al., 2010b ). Furthermore, the variability of the training tasks is important for generalizing training effects to untrained tasks (Sweller et al., 1998 ; Yamnill and McLean, 2001 ; Takeuchi et al., 2010b ). Moreover, when the same amount of training is conducted sparsely over a longer period, the training effects are retained for longer periods (Takeuchi et al., 2010b ). However, results of investigations determining whether sparse training is more effective than concentrated training are controversial (Takeuchi et al., 2010b ). Thus, these factors may have to be considered when training protocols are designed. Other factors that relate to the training effect are subject characteristics such as age and cognitive functions before the intervention. Ball et al. (2007) analyzed six studies that conducted PS training of visuospatial divided attention speed tasks. This analysis revealed that individual improvements in performance on the UFOV task, which is a main outcome measure and very similar to the training task, following the intervention were strongly and negatively correlated with performance on the UFOV task before the intervention (the worse the initial performance, the larger the training-related improvement). That study also revealed factors that can relate to a worse performance of the UFOV task before the intervention, such as older age, lower education level, and worse mental state (before the intervention) also positively correlated with training-related improvement in performance on the UFOV task. Furthermore, PS training of visuospatial divided s did not seem to improve driving-related measures in healthy older adults (Ball et al., 2002 ). However, when the training was conducted with healthy older adults and a risk of driving mobility decline, the training was effective in improving the driving-related measures (Edwards et al., 2009b ). However, this may have been the result of the sensitivity of the measure or ceiling effects caused by the better performance before the intervention. This is because a previous intervention study of WM training showed that older adults exhibit decline in neural plasticity and show fewer transfer effects (Dahlin et al., 2008 ). Finally, the training contents may affect the training effect described below. Various types of PS training tasks are available. In studies of WM training, training contents such as whether the training includes dual WM tasks may affect the training outcomes (Takeuchi et al., 2010b ). The same thing may be true for PS training, and PS training task factors such as stimulus type may affect training outcomes as described below. Effect of PS training on cognitive functions The effects of PS training on behavioral measures have been investigated in previous studies. The results of these studies showed that the effects of PS training on tasks similar to training tasks are robust. However, the effects of this training on PS measures or speeded tasks that are not similar to the training tasks are observed occasionally but are not very robust. Furthermore, previous studies on PS training rather consistently failed to identify an effect of PS training on the paper and pencil measures, which measure cognitive functions other than PS, non-speeded measures, and cognitive tasks well related to training tasks. However, this may depend on the type of training task. Furthermore, studies of PS training of visuospatial divided s, which are similar to the task that predicts driving performance as described above, showed PS training effects on driving-related measures and mental health in older adults. PS measures and near transfer Previous studies revealed that the effects of PS training on tasks similar to training tasks are robust. However, the effects of this training on PS measures or speed tasks that are not similar to the training tasks are observed occasionally but are not very robust (occasionally not observed and often statistically only marginally significant). Mackey et al. (2011) used a cognitive speed training program consisting of miscellaneous games for children, as

7 Processing speed training 295 described above. PS training in this study led to improvements in performance on standardized PS measures compared with the training with similar miscellaneous games that emphasized reasoning. Although we could not determine whether these improvements corresponded to near transfer or far transfer because there were many training tasks and details of the tasks were not described. Irausquin et al. (2005) investigated the effect of computerized training aimed at improving the speed of word identification and comprehension on children with poor reading abilities. Compared with control training, this training led to increased word and text reading efficiency. This training did not increase the accuracy of these tasks but led to increased speed of these tasks. It seemed that the effect of training was transferred to reading of more complex words than the trained ones and reading of the connected text of a higher complexity than the trained one. No effects were found for reading comprehension. These results suggest that speed training involving certain stimuli modalities can enhance the speed of processing information from those modalities. Our previous study (Takeuchi et al., 2011b ) used a 1-week intensive training program consisting of paper and pencil PS training tasks, computerized simple PS tasks, and computerized rapid recognition tasks for young adults. PS training in that study led to significant and substantial improvement in performance on simple PS tasks, which was unlike any of the training tasks. This training also led to improved performance on a complex arithmetic task but not on a simple arithmetic task in which subjects had to solve arithmetic problems as fast as possible. The training also led to substantial improvement in performance on an intelligence test consisting of speeded tasks. However, these improvements were either substantially or marginally statistically significant ( p = ) and must be replicated. Training involving visuospatial divided attention speed tasks shows a prominent effect on the nearest transfer tasks (the UFOV task). However, the effects of training involving these tasks on other PS measures are limited. These training tasks are very similar to the UFOV task. Edwards et al. (2002) showed that PS training of this type in older subjects with health problems led to clear improvements in performance on the UFOV task. It also led to a marginally significant improvement in performance on the timed instrumental activities of daily living (IADL) task, which measures speed in conditions that simulate everyday IADL ( p = 0.049, uncorrected for 16 tests). However, it did not lead to improvements in performance on the Road Sign Test, which measures daily visual search skills and visual speed in a driving simulator format. It also did not lead to improvements in performance on other paper and pencil PS measures such as Finding A s, identical pictures, letter and pattern comparison, and digit symbol substitution. Edwards et al. (2005) showed that PS training of this type in older subjects with PS difficulties led to improvements in performance on the UFOV and timed IADL tasks. However, it did not lead to improvements in performance on the Road Sign Test or other paper and pencil PS measures such as the letter comparison, pattern comparison, digit symbol substitution tasks, and Trail Making Test A. Vance et al. (2007) and Wadley et al. (2006) showed that PS training of this type in older subjects with PS difficulties led to improvements in performance on the UFOV task. However, it did not lead to improvements in performance on the Road Sign Test in either study. Roenker et al. (2003) showed that PS training of this type in older drivers with mobility risks led to improvements in performance on the UFOV task compared with driving simulator training. It also led to improvements in performance on the choice reaction time task in the driving simulator. However, it did not lead to improvements in performance on the simple reaction time task in the driving simulator. Ball et al. (2002) showed that PS training of this type in older healthy subjects led to improvements in performance on the UFOV task. However, although the PS training and control groups had > 700 subjects, PS training did not lead to significant improvement in daily speed composite score, which is calculated from two complex reaction time tasks and the timed IADL task after the training. The non-significance of the results of this study, which is in contrast to the results of the above-mentioned studies, may be a consequence of the characteristics of the subjects included in the study (healthy subjects without a PS decline risk). However, when follow-up concentrated training was conducted just before a follow-up evaluation, follow-up PS training had an effect on performance with regard to daily speed measures as well as the UFOV task (Ball et al., 2002 ; Willis et al., 2006 ). Differences in results can be attributed to many factors such as differences in training tasks. However, as more transfer effects are observed in studies with children and young adults as described above, higher plasticity in younger adults may be one of the reasons (Dahlin et al., 2008 ). Other performance cognitive measures The effects of PS cognitive training on cognitive domains other than PS may be less pronounced, and previous studies rather consistently failed to determine the effects of PS training on non-ps cognitive performance measures. Mackey et al. (2011) used a cognitive speed training program consisting of miscellaneous games for children. PS training in this study led to improvements in performance on standardized PS measures compared with the training with similar miscellaneous games that emphasized reasoning. By contrast, the training of reasoning in this study led to improvements in performance on standardized reasoning measures compared with the training with similar miscellaneous games that emphasized PS. There were no PS trainingrelated improvements in performance on WM cognitive measures. Our previous study (Takeuchi et al., 2011b ) used a 1-week intensive training program consisting of paper and pencil PS training tasks, computerized simple PS tasks, and computerized rapid recognition tasks in young adults. However, it did not lead to any non-verbal reasoning measures (Raven s Advanced Progressive Matrices and Cattell s Culture Fair

8 296 H. Takeuchi and R. Kawashima Test), WM measures (digit span and visuospatial span), inhibition measures (Stroop task and reverse Stroop task), or creativity measures (S-A creativity test). Edwards et al. (2002) showed that PS training of visuospatial divided s in older subjects with health problems did not lead to improvements in performance on (a) executive function measures such as the Stroop task and Trail Making Test B-A, (b) short-term memory measures such as the Benton Visual Retention Test, Rey-Osterrieth Complex Figure Test, and digit span, and (c) other measures such as the Block Design (visuospatial problem solving), Information task (crystallized intelligence) in the Wechsler Adult Intelligence Scale (WAIS) and the Facial Recognition. It also led to a significant decrease in performance on other verbal executive function measures (Controlled Oral Word Association), which may have been caused by multiple comparisons. Edwards et al. (2005) showed that PS training of visuospatial divided s in older subjects with PS difficulties did not lead to improvements in performance on (a) executive function measures such as the Stroop task and Trail Making Test B and (b) short-term memory measures such as digit span and spatial span. Vance et al. (2007) showed that PS training of visuospatial divided s in older subjects with PS difficulties led to improvements in performance on the Starry Night task (peripheral visual sensory function and attention over a spatial array and time) and the Rey-Osterrieth Complex Figure Copy Test (the visual-related task), although these results were not so statistically robust considering the number of multiple comparisons, and the authors suspended conclusions on the basis of the results of these tests. The training did not lead to improvements in performance on executive function measures (Trail Making Test B), short-term memory measures (Benton Visual Retention Test and Rey-Osterrieth Complex Figure Recall Test), and attention measures (Paced Auditory Serial Addition Test). Wadley et al. (2006) showed that PS training of visuospatial divided s in older subjects with PS difficulties did not lead to improvements in performance on executive function measures (Trail Making Test B) and shortterm memory measures (Benton Visual Retention Test). Ball et al. (2002) showed that PS training of visuospatial divided s in older healthy subjects did not lead to significant improvements in composite scores for daily problem solving and performance on the minimum data sethome care (MDS-HC), a measure of activities of daily living and IADL. This non-significance occurred despite > 700 subjects being enrolled in each of the PS training and control groups. However, when follow-up concentrated training was conducted just before the follow-up evaluation, follow-up PS training had a marginally significant ( p = 0.04, uncorrected) effect on performance on MDS-HC (Ball et al., 2002 ) as well as the IADL difficulty measure in MDS-HC (Willis et al., 2006 ). Effects of PS cognitive training on cognitive domains other than PS or near transfer tasks were not observed. However, when younger adults who exhibited higher plasticity and showed more transfer (Dahlin et al., 2008 ) were trained, speeded training tasks for complex cognitive tasks may lead to different results. In another study (H. Takeuchi et al., submitted for publication ), we used a training program consisting of paper and pencil fast simple numerical calculation training tasks and computerized fast simple numerical calculation tasks in a 1-week intensive training of young adults. This training led to improvements in performance on simple PS and speeded executive functioning measures (Stroop task; Takeuchi et al., 2012b ) together with performance on simple and complex arithmetic tasks. In another study (H. Takeuchi et al., submitted for publication ), we used a training program consisting of computerized multitask training tasks in a 4-week training program of young adults. This training led to improvements in performance on simple PS and speeded executive functioning measures (Stroop task; Takeuchi et al., 2012b ) and creativity task (Takeuchi et al., 2010c,d, 2011a,c ). However, some of these results were statistically fragile and warrant replication. Measures related to health and mental health Effects of PS training on health, quality of life, and mental health have been investigated in a series of studies that used PS training of visuospatial divided s. These studies revealed the effect of PS training on these measures in older adults. The ACTIVE study showed that compared with the control group, the group that underwent PS training of visuospatial divided s incurred significantly less predicted annual expenditures at 1 year after the training ($223; p = 0.024) ( et al., 2009a ). Furthermore, the study showed that compared with the control group, the group without suspected clinical depression who underwent PS training of visuospatial divided s incurred significantly less likelihood of developing suspected clinical depression at 1 year after the training (odds ratio = 0.62; p < 0.01) ( et al., 2009b ). However, the same study showed that compared with the control group, the group with suspected clinical depression who underwent PS training of visuospatial divided s did not exhibit significant recovery from suspected clinical depression ( et al., 2009b ). This lack of a significant difference may have resulted from a lack of statistical power because of sample size differences between the groups. In addition, the ACTIVE study showed that compared with the control group, the group that underwent PS training of visuospatial divided attention speed tasks exhibited lower chances for a clinically important increase in depressive symptoms at 1 and 5 years after the training ( et al., 2009c ). The study also showed that compared with the control group, the group that underwent PS training of visuospatial divided s was less likely to experience extensive health-related quality of life difficulties at 2 (odds ratio: 0.643, p = 0.004) ( et al., 2006b ) and 5 years after the training ( et al., 2006a ) and had significant improvements in self-rated health at 2, 3, and 5 years after the training. The significant improvements at 2 years were statistically robust ( p = 0.006). However, the ACTIVE study involved follow-up training (in a subset

9 Processing speed training 297 of subjects), and the above-mentioned results do not always indicate training effects that lasted for several years. Driving-related measures Effects of PS training on driving-related measures have been investigated in a series of studies that used PS training of visuospatial divided s. These studies revealed the effects of PS training on driving-related measures in older adults with a risk of cognitive or mobility decline. These training tasks were very similar to the UFOV task (Edwards et al., 2002 ). The UFOV task is a good predictor of daily performance and mobility outcomes among older adults (Ball and Owsley, 1993 ; Ball et al., 1993 ). Thus, the below-mentioned effects of PS training, despite the limited effects of PS training of this type on paper and pencil PS measures, might be attributed to the specific characteristic of these visuospatial divided attention speed training tasks. The ACTIVE study showed that the elderly group that underwent PS training of visuospatial divided attention speed tasks exhibited significantly fewer motor collisions in real life over a 6-year period after the training, which was approximately 50 % lower than that exhibited by the control group [collision rate ratio = 0.57, 95 % confidence interval (CI) = ] (Ball et al., 2010 ). Moreover, the ACTIVE and SKILL study data showed that the group that underwent PS training of visuospatial divided s had a marginally significant less likelihood to stop driving over a 3-year period after the training (cessation possibility ratio = 0.596, 95 % CI = ) (Edwards et al., 2009a ). Furthermore, the SKILL study data showed that the group that underwent PS training of visuospatial divided attention speed tasks consisting of drivers with a mobility risk showed less degradation in driving pace and driving exposure and fewer driving difficulties at 3 years after the training than the control group (Edwards et al., 2009b ). Roenker et al. (2003) showed that PS training of visuospatial divided s in older drivers with a mobility risk led to fewer dangerous maneuvers during a driving simulation compared with driving simulator training. The training led to worse performance on some driving simulator measures; however, it did not lead to significant changes in performance on other driving simulator measures. However, because these results were obtained when compared with driving simulator training, it does not mean that PS training of visuospatial divided s is ineffective. Ball et al. (2002) showed that PS training of visuospatial divided s in older healthy subjects did not lead to significant improvements in driving habits, although each group had > 700 subjects. It is perhaps difficult to detect the effects of PS training of healthy drivers in visuospatial divided s. Effects of PS training on neural systems In our previous study (Takeuchi et al., 2011b ), we used a 1-week intensive training program consisting of paper and pencil PS training tasks, computerized simple PS tasks, and computerized rapid recognition tasks in young adults and investigated the effect of simple PS training on neural systems. PS training in this study led to (a) increase in functional activity related to simple cognitive processes (but not those related to WM) in the left perisylvian region, (b) decrease in the gray matter structures of the left superior temporal gyrus and bilateral regions around the occipitotemporal junction, and (c) increase in resting FC between the left perisylvian area and the area that extends to the lingual gyrus and calcarine cortex. Neural changes in the left perisylvian area and left temporal gyrus may reflect training-related neural changes in linguistic and auditory processes. The neural changes in occipital areas may reflect neural changes in visual cognitive processes. This is because these regions are related to the above-mentioned cognitive processes and our training strongly involved all auditory, linguistic, and visuospatial components (Takeuchi et al., 2011b ). These results can be interpreted as: PS training affects brain areas involved in processing information from modalities related to the trained stimuli. Although this simple PS training did not lead to significant neural changes in the prefrontal regions, despite the association between PS and prefrontal regions, neural changes may depend on what type of speeded training tasks are used. In another study (H. Takeuchi et al., submitted for publication), we used a training program consisting of paper and pencil and computerized fast simple numerical calculation tasks in a 1-week intensive training program of young adults. This training was associated with reduction in regional gray matter volume (rgmv) and an increase in regional cerebral blood flow (rcbf) during rest in the frontopolar areas and a weak but widespread increase in resting rcbf in an anatomical cluster in the posterior region. Our previous functional magnetic resonance imaging (fmri) study showed that the prefrontal regions are increasingly recruited when more cognitively complex tasks are sped up than when cognitively simple tasks are sped up (Takeuchi et al., 2012a ). Taken together, complex speed training tasks may be associated more with prefrontal neural changes. Consistent with this notion, in another study (H. Takeuchi et al., submitted for publication), we used a training program consisting of computerized multitasking training tasks in a 4-week training program of young adults. This training led to (a) increased rgmv in three prefrontal cortical regions (left lateral rostral PFC, DLPFC, and the left inferior frontal junction) and the left posterior parietal cortex as well as (b) decreased resting FC between the right DLPFC and an anatomical cluster around the ventral anterior cingulate cortex (ACC). It was also associated with (c) increased resting rcbf in a weak but widely spreading anatomical cluster that included ACC and the right lateral PFC. Training protocols other than PS training that improve PS Some other training programs improve performance on PS tasks. One such program focuses on visual and auditory

10 298 H. Takeuchi and R. Kawashima selective attention (Mozolic et al., 2011 ). This training actively suppresses increasingly distracting background noise during task performance. Healthy older participants who participated in this training showed improvements in performance not only on selective attention measures but also on PS and dual task completion measures. This might be because selective attention is also important for performance on PS measures (Schear and Sato, 1989 ; Ellingsen et al., 2001 ). Furthermore, playing action video games results in improved PS across various perceptual and attentional tasks (Dye et al., 2009 ). For example, older subjects who played videogames, including Donkey Kong, developed improved reaction time (Clark et al., 1987 ). Uninvestigated issues As has been described, PS training with different stimuli or tasks can be considered to have different effects on psychological measures and neural mechanisms. Furthermore, the effects of PS training have been investigated for only one type of PS training for most measures, particularly non-performance psychological measures. Thus, the list of uninvestigated issues is diverse. We particularly focus on three issues in this section. First, PS training involving different stimuli and tasks has different effects, in particular cognitive domains, which are related to training tasks and stimuli. Furthermore, the adaptive procedures in PS training that modulate difficulties based on a subject s performance by speeding up tasks (or shortening stimuli presentation) can be applied to many cognitive tasks, including emotional and affective tasks. Thus, various types of cognitive domains including social and affective components can be improved through specific speed training, but these specific training tasks are yet to be developed. Second, the effects of PS training on the speed with which peak brain activity is attained and the duration for which brain activity lasts for each trial remain to be investigated. In the case of one form of training on multitasking (Dux et al., 2009 ), comparison of the brain activity response peak latency in a region of PFC indicated that the activity peaked 500 ms later under the dual task condition than under the single task condition prior to training. However, after training, no differences in the duration of brain activity were observed between the dual task and single task conditions, and these durations of activity for both conditions peaked earlier than those prior to training. These results indicate that training on multitasking reduces processing time in a region of PFC, leading to reduced dual task costs after training. In a similar manner, whether PS training reduces processing time and speed in relevant regions can be determined by analyzing the peak and duration of brain activity in those regions. Finally, the effects of PS training on white matter structural integrity have not yet been revealed. Simple PS has a close relationship with white matter structural integrity as described above. Our previous studies of WM training revealed that a 2-month WM training program causes plasticity in the white matter structural integrity of related regions (Takeuchi et al., 2010a ). However, a 1-week intensive PS training program consisting of paper and pencil PS training tasks, computerized simple PS tasks, and computerized rapid recognition tasks in young adults (Takeuchi et al., 2011b ) did not cause changes in the white matter structural integrity (unpublished data). Thus, longer duration of training may be required to cause structural plasticity in the white matter structural integrity. Conclusion Various types of PS training tasks are available, and the effects of PS training on the tasks similar to the training tasks are robust. However, the effects on PS measures or speeded tasks, which are not similar to the training tasks, are observed occasionally but are not very robust. Furthermore, previous studies of PS training rather consistently failed to find an effect of PS training on paper and pencil measures that measure cognitive functions other than PS or on non-speeded measures. However, this may depend on the type of training tasks. Moreover, a certain type of PS training improves driving-related measures and mental health in older adults. Neuroimaging studies of speed training have shown that the effects of speed training on neural mechanisms may vary depending on the training tasks and stimuli. Adaptive procedures to modulate difficulties of training tasks based on the subject s performance by modulating the task speed can be applied to various cognitive tasks and, perhaps, these procedures can be used to develop training protocols for enhancing various cognitive functions. Acknowledgements This study was supported by a Grant-in-Aid for Young Scientists (B) (KAKENHI ) from the Ministry of Education, Culture, Sports, Science, and Technology. The authors would like to thank Enago ( ) for the English language review. References Ball, K. and Owsley, C. (1993). The useful field of view test: a new technique for evaluating age-related declines in visual function. J. Am. Optom. Assoc. 64, Ball, K., Owsley, C., Sloane, M.E., Roenker, D.L., and Bruni, J.R. (1993). Visual attention problems as a predictor of vehicle crashes in older drivers. Invest. Ophthalmol. Vis. Sci. 34, Ball, K., Berch, D.B., Helmers, K.F., Jobe, J.B., Leveck, M.D., Marsiske, M., Morris, J.N., Rebok, G.W., Smith, D.M., and Tennstedt, S.L. (2002). Effects of cognitive training interventions with older adults: a randomized controlled trial. J. Am. Med. Assoc. 288, Ball, K., Edwards, J.D., and Ross, L.A. (2007). The impact of speed of processing training on cognitive and everyday functions. J. Gerontol. B: Psychol. Sci. Soc. Sci. 62B, Ball, K., Edwards, J.D., Ross, L.A., and McGwin, G., Jr. (2010). Cognitive training decreases motor vehicle collision involvement of older drivers. J. Am. Geriatr. Soc. 58,

11 Processing speed training 299 Benton, A.L. and Hamsher, A.K. (1983). Multilingual Aphasia Examination: Manual of Instructions (Iowa City, IA: AJA Associates). Benton, A.L., Sivan, A.B., Hamsher, A.K., Varney, N.R., and Spreen, O. (1994). Contributions to Neuropsychological Assessment: A clinical Manual, 2nd edition (New York: Oxford University Press). Br é bion, G., Amador, X., Smith, M.J., and Gorman, J.M. (1998). Memory impairment and schizophrenia: the role of processing speed. Schizophr. Res. 30, Buonomano, D.V. and Merzenich, M.M. (1998). Cortical plasticity: from synapses to maps. Annu. Rev. Neurosci. 21, Clark, J.E., Lanphear, A.K., and Riddick, C.C. (1987). The effects of videogame playing on the response selection processing of elderly adults. J. Gerontol. 42, Dahlin, E., Neely, A.S., Larsson, A., Backman, L., and Nyberg, L. (2008). Transfer of learning after updating training mediated by the striatum. Science 320, Demaree, H.A., DeLuca, J., Gaudino, E.A., and Diamond, B.J. (1999). Speed of information processing as a key deficit in multiple sclerosis: implications for rehabilitation. J. Neurol. Neurosurg. Psychiatry 67, Dux, P.E., Tombu, M.N., Harrison, S., Rogers, B.P., Tong, F., and Marois, R. (2009). Training improves multitasking performance by increasing the speed of information processing in human prefrontal cortex. Neuron 63, Dye, M.W.G., Green, C.S., and Bavelier, D. (2009). Increasing speed of processing with action video games. Curr. Dir. Psychol. Sci. 18, Earles, J.L. and Kersten, A.W. (1999). Processing speed and adult age differences in activity memory. Exp. Aging Res. 25, Edwards, J., Wadley, V., Myers, R., Roenker, D., Cissell, G., and Ball, K. (2002). Transfer of a speed of processing intervention to near and far cognitive functions. Gerontology 48, Edwards, J., Wadley, V., Vance, D., Wood, K., Roenker, D., and Ball, K. (2005). The impact of speed of processing training on cognitive and everyday performance. Aging Mental Health 9, Edwards, J.D., Delahunt, P.B., and Mahncke, H.W. (2009a). Cognitive speed of processing training delays driving cessation. J. Gerontol. A: Biol. Sci. Med. Sci. 64, Edwards, J.D., Myers, C., Ross, L.A., Roenker, D.L., Cissell, G.M., McLaughlin, A.M., and Ball, K.K. (2009b). The longitudinal impact of cognitive speed of processing training on driving mobility. Gerontologist 49, Ekstrom, R.B., French, J.W., Harman, H.H., and Derman, D. (1976). Kit of Factor-referenced Cognitive Tests (Princeton, NJ: Educational Testing Service). Ellingsen, D.G., Bast-Pettersen, R., Efskind, J., and Thomassen, Y. (2001). Neuropsychological effects of low mercury vapor exposure in chloralkali workers. Neurotoxicology 22, Flaherty, A.W. (2005). Frontotemporal and dopaminergic control of idea generation and creative drive. J. Comparat. Neurol. 493, Fry, A.F. and Hale, S. (2000). Relationships among processing speed, working memory, and fluid intelligence in children. Biol. Psychol. 54, Green, C.S. and Bavelier, D. (2008). Exercising your brain: a review of human brain plasticity and training-induced learning. Psychol. Aging 23, Gronwall, D.M. (1977). Paced auditory serial-addition task: a measure of recovery from concussion. Percept. Mot. Skills 44, Irausquin, R.S., Drent, J., and Verhoeven, L. (2005). Benefits of computer-presented speed training for poor readers. Ann. Dyslexia 55, Jobe, J.B., Smith, D.M., Ball, K., Tennstedt, S.L., Marsiske, M., Willis, S.L., Rebok, G.W., Morris, J.N., Helmers, K.F., and Leveck, M.D. (2001). Active: a cognitive intervention trial to promote independence in older adults. Control. Clin. Trials 22, Kail, R. and Salthouse, T.A. (1994). Processing speed as a mental capacity. Acta Psychol. (Amst.) 86, Karlinsky, H., Vaula, G., Haines, J., Ridgley, J., Bergeron, C., Mortilla, M., Tupler, R., Percy, M., Robitaille, Y., and Noldy, N. (1992). Molecular and prospective phenotypic characterization of a pedigree with familial Alzheimer s disease and a missense mutation in codon 717 of the β -amyloid precursor protein gene. Neurology 42, Klingberg, T. (2010). Training and plasticity of working memory. Trends Cogn. Sci. 14, Klingberg, T., Forssberg, H., and Westerberg, H. (2002). Training of working memory in children with ADHD. J. Clin. Exp. Neuropsychol. 24, Lee, C., Grossman, M., Morris, J., Stern, M.B., and Hurtig, H.I. (2003). Attentional resource and processing speed limitations during sentence processing in Parkinson s disease. Brain Lang. 85, Leskel ä, M., Hietanen, M., Kalska, H., Ylikoski, R., Pohjasvaara, T., M ä ntyl ä, R., and Erkinjuntti, T. (1999). Executive functions and speed of mental processing in elderly patients with frontal or nonfrontal ischemic stroke. Eur. J. Neurol. 6, Lezak, M.D. (1995). Neuropsychological Assessment, 3rd edition (New York: Oxford University Press). Mackey, A.P., Hill, S.S., Stone, S.I., and Bunge, S.A. (2011). Differential effects of reasoning and speed training in children. Dev. Sci. 14, Morris, J.N., Fries, B.E., Steel, K., Ikegami, N., Bernabei, R., Carpenter, G.I., Gilgen, R., Hirdes, J.P., and Topinkova, E. (1997). Comprehensive clinical assessment in community setting: applicability of the MDS-HC. J. Am. Geriatr. Soc. 45, Mozolic, J.L., Long, A.B., Morgan, A.R., Rawley-Payne, M., and Laurienti, P.J. (2011). A cognitive training intervention improves modality-specific attention in a randomized controlled trial of healthy older adults. Neurobiol. Aging 32, Northam, E.A., Anderson, P.J., Jacobs, R., Hughes, M., Warne, G.L., and Werther, G.A. (2001). Neuropsychological profiles of children with type 1 diabetes 6 years after disease onset. Diabetes Care 24, Nouchi, R., Taki, Y., Takeuchi, H., Hashizume, H., Akitsuki, Y., Shigemune, Y., Sekiguchi, A., Kotozaki, Y., Tsukiura, T., Yomogida, Y., and Kawashima, R. (2012). Train your cognitive functions in minutes a day; brain training game improves executive functions and processing speed in the elderly. PLoS ONE 7, e Ollo, C., Johnson, R., and Grafman, J. (1991). Signs of cognitive change in HIV disease: an event-related brain potential study. Neurology 41, Poldrack, R.A., Temple, E., Protopapas, A., Nagarajan, S., Tallal, P., Merzenich, M., and Gabrieli, J.D.E. (2001). Relations between the neural bases of dynamic auditory processing and phonological processing: evidence from fmri. J. Cognit. Neurosci. 13, Rao, S.M., Aubin-Faubert, P.S., and Leo, G.J. (1989). Information processing speed in patients with multiple sclerosis. J. Clin. Exp. Neuropsychol. 11,

12 300 H. Takeuchi and R. Kawashima Raven, J. (1998). Manual for Raven s Progressive Matrices and Vocabulary Scales. (Oxford: Oxford Psychologists Press). Rizzo, M. and Robin, D.A. (1996). Bilateral effects of unilateral visual cortex lesions in human. Brain 119, Rizzo, M., Reinach, S., McGehee, D., and Dawson, J. (1997). Simulated car crashes and crash predictors in drivers with Alzheimer disease. Arch. Neurol. 54, Roenker, D.L., Cissell, G.M., Ball, K.K., Wadley, V.G., and Edwards, J.D. (2003). Speed-of-processing and driving simulator training result in improved driving performance. Human Factors 45, Rypma, B., Berger, J.S., Prabhakaran, V., Martin Bly, B., Kimberg, D.Y., Biswal, B.B., and D Esposito, M. (2006). Neural correlates of cognitive efficiency. Neuroimage 33, Salthouse, T.A. (1991). Mediation of adult age differences in cognition by reductions in working memory and speed of processing. Psychol. Sci. 2, Salthouse, T.A. (1996). The processing-speed theory of adult age differences in cognition. Psycholog. Rev. 103, Schear, J.M. and Sato, S.D. (1989). Effects of visual acuity and visual motor speed and dexterity on cognitive test performance. Arch. Clin. Neuropsychol. 4, Schmiedek, F., Lövdén, M., and Lindenberger, U. (2010). Hundred days of cognitive training enhance broad cognitive abilities in adulthood: findings from the COGITO study. Front. Aging Neurosci. 2, Article 27. Sheline, Y.I., Barch, D.M., Garcia, K., Gersing, K., Pieper, C., Welsh-Bohmer, K., Steffens, D.C., and Doraiswamy, P.M. (2006). Cognitive function in late life depression: relationships to depression severity, cerebrovascular risk factors and processing speed. Biol. Psychiatry 60, Shucard, J.L., Parrish, J., Shucard, D.W., McCabe, D.C., Benedict, R.H.B., and Ambrus, J. (2004). Working memory and processing speed deficits in systemic lupus erythematosus as measured by the paced auditory serial addition test. J. Int. Neuropsychol. Soc. 10, Stuss, D.T., Murphy, K.J., Binns, M.A., and Alexander, M.P. (2003). Staying on the job: the frontal lobes control individual performance variability. Brain 126, Sweller, J., Van Merrienboer, J.J.G., and Paas, F. (1998). Cognitive architecture and instructional design. Educ. Psychol. Rev. 10, Takeuchi, H., Sekiguchi, A., Taki, Y., Yokoyama, S., Yomogida, Y., Komuro, N., Yamanouchi, T., Suzuki, S., and Kawashima, R. (2010a). Training of working memory impacts structural connectivity. J. Neurosci. 30, Takeuchi, H., Taki, Y., and Kawashima, R. (2010b). Effects of working memory training on cognitive functions and neural systems. Rev. Neurosci. 21, Takeuchi, H., Taki, Y., Sassa, Y., Hashizume, H., Sekiguchi, A., Fukushima, A., and Kawashima, R. (2010c). Regional gray matter volume of dopaminergic system associate with creati vity: evidence from voxel-based morphometry. Neuroimage 51, Takeuchi, H., Taki, Y., Sassa, Y., Hashizume, H., Sekiguchi, A., Fukushima, A., and Kawashima, R. (2010d). White matter structures associated with creativity: evidence from diffusion tensor imaging. Neuroimage 51, Takeuchi, H., Taki, Y., Hashizume, H., Sassa, Y., Nagase, T., Nouchi, R., and Kawashima, R., (2011a). Cerebral blood flow during rest associates with general intelligence and creativity. PLoS ONE 6, e Takeuchi, H., Taki, Y., Hashizume, H., Sassa, Y., Nagase, T., Nouchi, R., and Kawashima, R., (2011b). Effects of training of processing speed on neural systems. J. Neurosci. 31, Takeuchi, H., Taki, Y., Hashizume, H., Sassa, Y., Nagase, T., Nouchi, R., and Kawashima, R. (2011c). Failing to deactivate: the association between brain activity during a working memory task and creativity. Neuroimage 55, Takeuchi, H., Sugiura, M., Sassa, Y., Sekiguchi, A., Yomogida, Y., Taki, Y., and Kawashima, R., (2012a). Neural correlates of the difference between working memory speed and simple sensorimotor speed: an fmri study. PLoS ONE 7, e Takeuchi, H., Taki, Y., Sassa, Y., Hashizume, H., Sekiguchi, A., Nagase, T., Nouchi, R., Fukushima, A., and Kawashima, R. (2012b). Regional gray and white matter volume associated with Stroop interference: evidence from voxel-based morphometry. Neuroimage 59, Tallal, P., Miller, S.L., Bedi, G., Byma, G., Wang, X., Nagarajan, S.S., Schreiner, C., Jenkins, W.M., and Merzenich, M.M. (1996). Language comprehension in language-learning impaired children improved with acoustically modified speech. Science 271, Tuch, D.S., Salat, D.H., Wisco, J.J., Zaleta, A.K., Hevelone, N.D., and Rosas, H.D. (2005). Choice reaction time performance correlates with diffusion anisotropy in white matter pathways supporting visuospatial attention. Proc. Natl. Acad. Sci. USA 102, Van den Heuvel, D., Ten Dam, V., De Craen, A., Admiraal-Behloul, F., Olofsen, H., Bollen, E., Jolles, J., Murray, H., Blauw, G., and Westendorp, R. (2006). Increase in periventricular white matter hyperintensities parallels decline in mental processing speed in a non-demented elderly population. J. Neurol. Neurosurg. Psychiatry 77, Vance, D., Dawson, J., Wadley, V., Edwards, J., Roenker, D., Rizzo, M., and Ball, K. (2007). The accelerate study: the longitudinal effect of speed of processing training on cognitive performance of older adults. Rehabil. Psychol. 52, Wadley, V.G., Benz, R.L., Ball, K.K., Roenker, D.L., Edwards, J.D., and Vance, D.E. (2006). Development and evaluation of homebased speed-of-processing training for older adults. Arch. Phys. Med. Rehabil. 87, Wechsler, D. (1997). WAIS-III Administration and Scoring Manual (San Antonio, TX: The Psychological Corporation). Willis, S.L., Tennstedt, S.L., Marsiske, M., Ball, K., Elias, J., Koepke, K.M., Morris, J.N., Rebok, G.W., Unverzagt, F.W., and Stoddard, A.M. (2006). Long-term effects of cognitive training on everyday functional outcomes in older adults. J. Am. Med. Assoc. 296, , F.D., Unverzagt, F.W., Smith, D.M., Jones, R., Stoddard, A., and Tennstedt, S.L. (2006a). The ACTIVE cognitive training trial and health-related quality of life: protection that lasts for 5 years. J. Gerontol. A: Biol. Sci. Med. Sci. 61, , F.D., Unverzagt, F.W., Smith, D.M., Jones, R., Wright, E., and Tennstedt, S.L. (2006b). The effects of the ACTIVE cognitive training trial on clinically relevant declines in healthrelated quality of life. J. Gerontol. B: Psychol. Sci. Soc. Sci. 61B, S281 S287., F., Mahncke, H., Kosinski, M., Unverzagt, F., Smith, D., Jones, R., Stoddard, A., and Tennstedt, S. (2009a). The ACTIVE cognitive training trial and predicted medical expenditures. BMC Health Serv. Res. 9, 109., F.D., Mahncke, H.W., Weg, M.W.V., Martin, R., Unverzagt, F.W., Ball, K.K., Jones, R.N., and Tennstedt, S.L. (2009b). The ACTIVE cognitive training interventions and the onset of and recovery from suspected clinical depression. J. Gerontol. B: Psychol. Sci. Soc. Sci. 64,

13 Processing speed training 301, F.D., Vander Weg, M.W., Martin, R., Unverzagt, F.W., Ball, K.K., Jones, R.N., and Tennstedt, S.L. (2009c). The effect of speed-of-processing training on depressive symptoms in ACTIVE. J. Gerontol. A: Biol. Sci. Med. Sci. 64, , F.D., Mahncke, H., Vander Weg, M.W., Martin, R., Unverzagt, F.W., Ball, K.K., Jones, R.N., and Tennstedt, S.L. (2010). Speed of processing training protects self-rated health in older adults: enduring effects observed in the multi-site ACTIVE randomized controlled trial. Int. Psychogeriatr. 22, Woodcock, R.W., (1997). The Woodcock-Johnson tests of cognitive ability Revised. In: Flanagan, D.P. and Genshaft, J.L. (eds.) Contemporary Intellectual Assessment: Theories, Tests, and Issues (New York: Guilford Press), pp Yamnill, S. and McLean, G.N. (2001). Theories supporting transfer of training. Human Resource Dev. Q. 12, Received February 25, 2012; accepted April 11, 2012 ; previously published online May 23, 2012 Dr. Hikaru Takeuchi is an Assistant Professor of the Smart Ageing International Research Center, Institute of Development, Aging and Cancer, Tohoku University from His scientific interest is in brain mapping of neural bases and plasticities of higher cognitive functions of humans. He won The Sagisaka Memorial Award of the Gonryo Medical Foundation in His scientific output includes 13 peer reviewed original articles and two review papers as the first author as well as other 14 original articles as a co-author. Dr. Ryuta Kawashima has been a Professor of the Department of Functional Brain Imaging, Institute of Development, Aging and Cancer (IDAC), Tohoku University since 2006, and a Director of the Smart Ageing International Research Center, IDAC, Tohoku University since His scientific interest is in functional brain mapping of higher cognitive functions of humans. He has won the Prize for Science and Technology, by the Japanese Ministry of Education, Culture, Sports, Science and Technology in His scientific output includes over 200 peer reviewed papers and the 150 books.