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Understanding Dyslexia Part 3: Processing Speed Abnormalities Exercising the brain to improve memory, focus, Belly fat loss daily functionality Increses a Healthy energy capsules priority for many older adults. But Incresaes of all ages Accuracy benefit from incorporating a few simple brain exercises Increaaes their Insulin control strategies life. The brain is involved in everything we do, and, like any Inceeases part of Belly fat loss body, it acciracy to be cared for too. Research has shown that there are many ways you can hone your mental sharpness and help your brain stay healthy, no matter what age you are. Doing certain brain exercises to help boost your memoryconcentration, and focus can make daily tasks quicker and easier to do, and keep your brain sharp as you get older. Research has shown that doing jigsaw puzzles recruits multiple cognitive abilities and is a protective factor for visuospatial cognitive aging. In other words, when putting together a jigsaw puzzle, you have to look at different pieces and figure out where they fit within the larger picture.Increases mental speed and accuracy -
The brain forms new pathways while you sleep, and studies have shown that getting the right amount of sleep helps with learning, problem-solving skills, and memory. National Institutes of Health Go to source Sleep deficiency—even losing just 1 to 2 hours across several nights—can affect reaction time and processing time, making it longer to finish tasks.
Try nootropics. Nootropics are over-the-counter supplements that aim to improve brain function. Some popular nootropics include: [11] X Trustworthy Source PubMed Central Journal archive from the U. National Institutes of Health Go to source Bacopa Monnieri Brahmi plant extract L-theanine Racetams Artichoke extract Ginseng.
Keep learning new things. Well into adulthood, the brain continues making new connections. Learning a task builds new connections, and mastering those connections strengthens the connections in a way that makes the information travel across axons more quickly.
Thicker myelin sheaths can improve a number of brainy tasks from reading to creating memories to decision-making. National Institutes of Health Go to source.
Start playing an instrument. Some studies have shown that taking up an instrument can develop strong connections across different areas of the brain. Maintain social networks. Not necessarily the online variety, but maintaining an active social life is good for your cognitive abilities.
Lively discussion necessitates quick thinking, and maintaining strong social networks is a great way to challenge yourself mentally and keep you on your toes. Stop smoking. If you currently smoke, then strongly think about quitting.
Beyond what smoking means for your risks of cancer and emphysema, it has also been shown to decrease overall brain volume, especially in the hippocampus. Smokers lose brain volume faster than nonsmokers, which can affect cognitive ability. Method 2. Enlarge this picture by opening it in a new window.
Brain games have been shown to sometimes have an effect on cognitive ability. The rise in popularity of brain games is too new for any long-term studies to prove specific improvements of brain function; however, many people feel that brain games have helped with their cognition.
As Lewis reiterates, no game or activity is better than another. Call out the direction the eyes are looking in the picture. Starting at the top, move from left to right and top to bottom while calling out the direction the eyes are looking down, left, up, right.
Time yourself as you go through the faces, and try to do it without errors, first within 30 seconds, then within As with the first trial, time yourself and try to complete the picture without error at different milestones—30 seconds, then 15 seconds.
Go back to the first exercise but include opposite finger-pointing. Similar to the idea behind why musical training can improve processing speed, this exercise requires the coordination of multiple brain modalities—visual to interpret the information in the picture, verbal to say the direction aloud, and motor to point in coordination with the rest.
You can also continue adding more components to the exercises. Expand your repertoire of brain games. This is just one example of a brain game. Many websites are dedicated to timed games for reasoning, memory, and processing speed. Luminosity, Brainist, Fit Brains, and Games for the Brain are all examples of websites dedicated to brain training.
Outside of websites, you can also find brain-training app games for your mobile devices and games such as Brain Age for the Nintendo DS. Sarah Gehrke, RN, MS Registered Nurse. Sarah Gehrke, RN, MS. Always question everything.
Don't take information on authority until you've investigated it yourself. Practice adjusting your perspective. Try to think several moves ahead. Read more books, especially on subjects you normally wouldn't read!
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Support wikiHow Yes No. Not Helpful 11 Helpful Get plenty of exercise in combination with a healthy diet and a good night's sleep. Always keep learning new things. If you smoke, stop and increase physical exercise. Not Helpful 12 Helpful Include your email address to get a message when this question is answered.
Submit a Tip All tip submissions are carefully reviewed before being published. Thanks Helpful 0 Not Helpful 0. You Might Also Like. How to. More References About This Article. Medically reviewed by:. Michael Lewis, MD, MPH, MBA, FACPM, FACN. b Latency difference between irrelevant and relevant trials in a with positive values denoting higher latencies in irrelevant trials.
Asterisks indicate a value significantly different from 0. Two values in the reward condition are outside the plotted range ms, ms. The bottom panels in Fig. Common across all conditions is that response accuracy increases with reaction time and reaches an asymptote approximately ms after target onset.
We compared these time courses of proportion correct responses for relevant versus irrelevant trials in the respective blocks Fig. The cluster was found in a time window 16— ms after target onset Fig.
During this time window, performance was superior for relevant compared with irrelevant trials. a — c The lower panels show accuracy time courses for relevant green and irrelevant blue trials in the three blocks, respectively.
Each time course shows the proportion of saccades to the correct disc, the target, as a function of saccade latency. Gray horizontal lines and asterisk indicate a significant cluster.
Upper panels show reaction time histograms pooled across all participants. d — f Accuracy time courses comparing the relevant conditions i.
The opposite was found in the task block Fig. Furthermore, comparing relevant trials in the reward block with those from the task block green line in Fig. No such difference was observed when comparing relevant and irrelevant trials from the face block Fig.
Aggregated across time, we observed no significant performance benefit for relevant compared with irrelevant trials in the reward block 0.
Thus, due to the reaction time difference, a higher number of responses in relevant trials were carried out in a time window where an incorrect response was more likely. In the task block, we observed a lower response accuracy for relevant compared with irrelevant trials 0.
In sum, our results are most consistent with i faster performance when expecting a monetary reward and an accuracy benefit for early responses, ii faster but less accurate performance with a perceptual task, and iii no difference in either speed or accuracy when seeing a human face or an otherwise irrelevant grating.
In a next step, we wanted to know what determines the differences in accuracy. To this end, we analyzed the time course of errors Figs. Particularly, we looked at two different kinds of errors. First, responses to the distractor disc. Second, we analyzed saccadic responses to the opposing disc.
Given that distractor and target disc were always next to each other, knowing the location of the distractor renders two discs possible target locations. We refer to this second disc who did not turn into the target as opposing disc righthand panel in Fig.
These errors would reflect target anticipation and thus be possibly indicative of strategic behavior. Hence, errors might be due to this strategic gambling behavior.
Error analysis. Proportion of erroneous responses over time for relevant green and irrelevant blue trials in the three blocks. The upper row shows trials in which the distractor was selected, whereas the lower row shows trials in which the disc opposing the target was selected.
Given that the onset of the distractor renders two discs possible target locations the target disc and the opposing disc , the latter can be seen as an index of strategic anticipation. Error analysis across blocks.
Top row: Time courses for erroneous responses to the distractor comparing the relevant conditions i. Bottom row: Time courses for erroneous responses to the opposing disc comparing merged data from relevant and irrelevant trials i.
Figure 4 shows error time courses for responses to the distractor disc upper row and responses to the opposing disc lower row. The proportion of trials in which the distractor was selected as saccade target is high for early responses and then decreases with increasing latency.
In the reward block we found a significant cluster in a time window 20— ms after target onset Fig. During this cluster, there were fewer error responses for relevant compared with irrelevant trials. This is consistent with the pattern observed in accurate trials Fig.
In the task block, error time courses were also consistent with the accuracy data. The corresponding cluster was detected — ms after target onset. In this time window, we observed more errors in trials with a perceptual task relevant than without irrelevant.
In the face block, no cluster and thus no difference between relevant and irrelevant trials was detected.
When comparing relevant trials of different blocks in term of erroneous responses to the distractor Fig. In each case, fewer errors were found in the reward block during the detected clusters.
For error responses to the opposing disc, no clusters were detected when comparing relevant and irrelevant trials in each of the three blocks. This analysis was restricted to the first ms after target onset, because hardly any of these errors occurred after this time point. However, we observed a difference between blocks Fig.
In both cases, erroneous responses to the opposing disc were more pronounced in the reward block. To summarize, we found more errors due to strategic anticipation in the reward block than in the other two blocks Fig. Yet, relevant trials of the reward block were characterized by fewer erroneous responses to the distractor Figs.
The difference in response accuracy between relevant and irrelevant trials Fig. Speed—accuracy trade-offs can be captured by sequential sampling models. We therefore complemented our analysis with a drift diffusion modelling approach to reveal how the different consequences affect latent decision variables.
The model assumes that evidence starts to accumulate in between two boundaries until one of the boundaries is reached. The systematic component of the evidence accumulation process is called the drift rate. It denotes the mean evidence uptake per time.
Yet evidence accumulation is noisy. Therefore, even if the drift rate favors one of the two decision outcomes, the other threshold can be reached first due to the noise. Therefore, the information uptake is sometimes referred to as the ease of processing. Decision threshold on the other hand is reflected in the boundary separation parameter.
This parameter can be affected by instructing the participant to either emphasize speed or accuracy. Thus, the boundary separation parameter captures trade-offs in speed and accuracy. Increasing the boundary separation would reduce the number of errors but would also increase reaction times.
The other two main parameters are the starting point and the nondecision time. The former can capture response biases that can occur if one of the response options is more likely or associated with a higher payoff e.
In our paradigm this would have been the case if the target was not equally distributed across the four discs or if one of the discs was associated with a higher reward than the other discs.
The nondecision time parameter is thought to capture all aspects of the reaction time that is not devoted to the decision itself but devoted to other processes, like sensory encoding and motor execution. However, the nondecision time has been reported to be also affected when accuracy or speed is emphasized Dutilh et al.
Drift diffusion model. a Illustration of the drift diffusion model. The model assumes that a response is made once the accumulation process reaches either of two boundaries. Each boundary is associated with a different response here: correct response versus error. The systematic component of the drift process is the drift rate i.
The thin orange line denotes an example trial, and the colored areas denote latency distributions for correct trials orange and errors red.
b , c , d Violin plots of drift rate b , boundary separation c , and nondecision time parameter d. Gray lines and asterisks indicate a significant different between relevant and irrelevant trials of a particular block.
We fit the full drift diffusion model to the data and allowed drift rate, boundary separation and nondecision time to vary across condition. To assess whether our manipulation affected decision thresholds, we compared boundary separation parameters using a 3 × 2 repeated-measures ANOVA Fig.
We additionally analyzed nondecision time parameters because emphasizing accuracy has been shown to also affect nondecision times in addition to the boundary separation Dutilh et al.
The observed pattern in nondecision times Fig. Again, we found no difference between the two conditions of the face block face vs. This suggests that relevant and irrelevant trials differed in decision thresholds and thus that participants behaved less cautious when they expected a monetary reward or a perceptual task.
We next analyzed drift rate parameters to reveal whether conditions differ in information uptake Fig. Typically, drift rates are expected to be higher when the task easy, for example because the target has a higher contrast and can be more easily processed. In sum, drift diffusion modelling revealed that relevant and irrelevant trials of the reward block reward vs.
no reward and task block task vs. no task differed in decision thresholds. Participants emphasized speeded responses when they expected a perceptual task or a monetary reward.
This was reflected in the boundary separation parameter. The same pattern was observed in nondecision times. On the other hand, a difference in information uptake between relevant and irrelevant trials was only observed in the reward block.
This was reflected in drift rates. To test whether the behavioral results can be explained by differences in distractor suppression, we analyzed saccade deviation as a function of distractor position. We made use of the fact that long-latency saccades curve away from distractors McSorley et al.
If the distractor in our paradigm is suppressed, we would thus expect that saccades deviate away from it, because of differences in saccadic end points as well as saccadic curvature.
Given that the distractor preceded the target by ms and did not appear simultaneously, even short-latency saccade showed characteristics of deviation away Fig. Please note that this measure of deviation jointly codes deviation due to saccade curvature McSorley et al.
In a second step, we normalized saccade duration to have the same number of data points for each saccade. In a third step, we computed the area under the saccade trajectory as an index of deviation shaded areas in Fig.
We coded deviation indices so that positive values always denote deviation away from the distractor. Saccades deviate away from distractor locations. a Saccade trajectories of all conditions to the four different target locations when the distractor was at a neighboring location, i.
The data was coded relative to saccade starting points. b Computation of deviation index. Saccades were normalized in length and rotated such that the straight connection between fixation cross and target center was purely horizontal. Consequently, any deviation in saccade trajectories can be found along the vertical dimension.
For each saccade, we computed a deviation index as the area under the saccade trajectory blue and orange shaded area. For a counterclockwise distractor, values were recoded multiplied with -1 so that positive deviation indices denote deviation away from the distractor.
c Violin plots of the mean deviation index in the respective conditions. Indices were different from 0 and larger in the reward block. d Deviation indices as a function of saccade latency SMART analysis comparing relevant green and irrelevant trials blue from the respective blocks.
e Comparison of merged data from relevant and irrelevant trials i. Gray lines and asterisks denote a significant cluster. Deviation indices Fig.
We next analyzed deviation indices as a function of saccade latency. Consistent with the ANOVA on the aggregated values, we observed no difference between relevant and irrelevant trials in any of the three blocks Fig. If motivation by reward increases speed and accuracy by improving distractor suppression, then we would expect that performance only differs when the distractor is present.
If, however, we observe a reward benefit in trials with and without distractor, this would be evidence that motivation by reward improves performance by improving target facilitation. Figure 8a shows saccade latencies and accuracy time courses for distractor present versus absent trials.
The latency results are thus more consistent with the idea that reward improves distractor suppression. Experiment 2: Target facilitation vs. distractor suppression. a Violin plot of saccade latencies when the distractor was present or absent, both for the condition with reward green and without blue.
Gray dots represent individual values whereas black lines indicate the aggregated mean. The asterisk and horizontal gray line denote a significant comparison. b , c Time course analysis for distractor present b and absent trials c. Each time course shows the proportion of saccades to the correct disc as a function of saccade latency.
d Violin plot of the deviation index in rewarded green and unrewarded trials blue. e Deviation index as a function of saccade latency SMART analysis.
Accuracy time courses can be found in Fig. Whereas performance was at ceiling in distractor-absent trials, time courses increased starting from around 80 ms after target onset when the distractor was present. No cluster and thus no difference between time courses was detected when the distractor was absent.
Consistent with Experiment 1, we computed deviation indices when the distractor was at a neighboring position. Thus, this analysis can only be conducted for distractor-present trials. This was also true when deviation indices were analyzed as a function of saccade latency Fig.
No cluster was detected, and we thus observed no difference between rewarded and unrewarded trials. The present results show that only motivation by reward can simultaneously increase response speed and accuracy and is thus capable of decreasing internal noise Manohar et al.
Obtaining task-relevant information increased speed but decreased accuracy, a pattern consistent with the traditional speed—accuracy trade-off. To distinguish whether motivation by reward facilitated target processing or aided with distractor suppression Wöstmann et al. Moreover, we conducted Experiment 2, where we randomly interleaved trials with and without distractor and kept distractor and target spatially independent.
We again analyzed accuracy, latencies, and saccadic deviation. No difference in accuracy time courses between rewarded and unrewarded was observed, neither when the target was absent, nor when it was present. In distractor-absent trials, this can be attributed to a ceiling effect.
In distractor present trials this might be explained by the more difficult task compared with Experiment 1 and the decreased consistency between participants. Hence, accuracy data neither favored target facilitation nor distractor suppression.
For saccade latencies, we found that a benefit in speed could only be observed when the distractor was present Fig. This is more consistent with the notion that motivation by reward aided distractor suppression.
This can most likely be explained the fact that the distractor preceded the target in our experiment. Hence, in distractor-absent trials there was uncertainty whether participants should respond to the onset of a peripheral target, because they would first have to discriminate whether this is a distractor or a target.
There was no such uncertainty in distractor present trials: If the distractor was already present, then participants knew that they could respond the upcoming stimulus.
Saccade trajectories in both experiments deviated away from distractor locations Figs. Deviation was stronger in the reward block compared with the other two blocks Fig. If the performance difference between rewarded and unrewarded trials was caused by improved distractor suppression, we would have expected stronger saccadic deviation in trials with a reward.
Even if our results cannot ultimately distinguish whether reward facilitates target processing or whether it improves distractor suppression, two further observations from the main experiment support the latter: First, most errors were erroneous responses to the distractor Figs. The proportion of erroneous responses to the distractor was least in rewarded trials compared with any other condition Figs.
Second, a performance benefit was observed for latencies within the first ms after target onset Figs. Taken together, although we cannot ultimately distinguish whether reward aids target facilitation or whether it improves distractor suppression, our results are more consistent with the latter.
We analyzed accuracy and occurrence of specific errors as a function of response time Figs. We found higher accuracy in rewarded trials within the first ms after target onset, and a lowered accuracy with a perceptual task for responses initiated between and ms.
Most errors were caused by premature responses to the distractor rather than an active gambling behavior Figs. However, the lacking difference in strategic errors between relevant and irrelevant trials might be caused by the few errors and the few trials in the time window where these errors mostly occurred.
The common analysis of mean response time and mean accuracy on the one hand and these time courses on the other hand indicates whether changes in behavior are caused by a trade-off between speed and accuracy or by processes that operate outside this trade-off. Theoretically, if two conditions differed due to a traditional speed—accuracy trade-off, this would result in different mean response times and accuracies without a change in the time courses of these two conditions.
Indeed, if changes in performance are time-locked to stimulus onset, then the underlying time course should be the same. Thus, changes in speed and accuracy would only result from the way that this time course is sampled.
For example, imagine overlapping time courses as in Fig. This would result in different mean response times and accuracies despite the same underlying time course. Contrary to that, differences in the time courses reflect processes beyond the speed—accuracy trade-off.
For example, a shifted time course might be indicative of reduced internal noise that might result in better distractor suppression or facilitated target processing. Yet, even if the two time courses differ, performance might still be prone to a trade-off between speed and accuracy.
We believe that this can account for our results in the reward condition: At large, our results in the reward condition are consistent with the findings obtained by Manohar et al.
We observed that the prospect of reward resulted in more accurate responses shortly after target onset. However, this did not show on the aggregated level mean accuracies, i. We believe that this is due to two effects cancelling each other out.
The first is an earlier saturation of accuracy in rewarded compared with unrewarded trials Fig. This first effect would yield better performance in rewarded compared with unrewarded trials. The second effect assumes that behavior in the reward condition is still prone to the classical trade-off between speed and accuracy.
Response accuracy increased steadily and saturated at a time point where most responses had not yet occurred. In turn, this also implies that most responses occurred at a time point without a benefit for rewarded over unrewarded trials. Thus, the increased speed in rewarded trials led to a higher fraction of trials with response times at which performance was not yet saturated.
Whether the performance benefit in rewarded trials can be observed on the aggregated level will thus also depend on the fraction of trials that occur in the time window where performance is enhanced. This might have been the case if we had decided to use a shorter delay between distractor and target, for example a time between 40 and ms as in Manohar et al.
In this line of thought, mean response times and accuracies in the task-relevance condition indicated a typical speed—accuracy trade-off: faster, yet less accurate selection in relevant compared with irrelevant trials.
This was reflected in a lowered decision threshold in the diffusion model. Thus, participants were less cautious so that they may see the perceptual target earlier, accepting potential errors. This pattern did not only result from the way that the same underlying time course was sampled, because accuracy time courses differed between relevant and irrelevant trials Fig.
The time course in relevant trials showed a late dip in performance after the initial distractor suppression had already been saturated. Such lapses cannot be attributed to a strategic task avoidance to reduce the overall experimental duration: If participants selected the wrong target, they still had to perform the perceptual task, making it impossible to speed up the experiment by strategically selecting a disc other than the target.
Instead, these lapses might be indicative of a diminished task engagement and a lack in motivation to perform well in the task. Postsaccadic vision of an intrinsically relevant face stimulus did neither affect response speed nor accuracy.
In this condition, we used faces of famous people to enable person recognition. Although participants might not have been familiar with all faces, recognition should have been possible in the majority of trials. Hence, our data suggest that neither postsaccadic vision of an intrinsically relevant face stimulus, nor the possibility to recognize a face have the motivational ability to affect oculomotor target selection.
Even short peripheral glimpses of the target might suffice to induce faster saccades towards faces, even if the saccade is carried out at a later point in time Xu-Wilson et al. We complemented our analysis with a drift-diffusion modelling approach which showed that motivation by reward affected information uptake i.
According to the selective influence assumption, instructing participants to either emphasize speed or accuracy should only affect decision thresholds, whereas changing the difficulty of the task should exclusively affect information uptake.
Under this assumption, our results are consistent with the conclusion that i reward and task-relevance affect the speed—accuracy trade-off, whereas image content does not. Additionally, ii motivation by reward increases the amount of information per time, effectively making the task easier when anticipating a reward.
This is consistent with the notion that motivation by reward can decrease noise Manohar et al. However, this selective influence assumption has recently been challenged Dutilh et al.
Our present results show that the same pattern that was observed in the boundary separation parameter could also be found in nondecision times.
Nondecision times are thought to reflect that part of the reaction time that is not devoted to the decision processes but to other processes such as the motor response or stimulus encoding. Our measure of reaction time, saccade latencies, does not include the time dedicated to the actual movement and there is no reason to assume any difference in the encoding time of the presaccadic display.
Hence, this result pattern shows that nondecision times covaried with changes in decision thresholds which appears inconsistent with the selective influence assumption. However, we did not explicitly instruct participants to either emphasize speed or accuracy, but we manipulated the consequences following an accurate response to test whether participants implicitly adjust their trade-off in speed and accuracy.
Thus, participants were free to adjust their behavior in any way and we cannot distinguish whether our results are inconsistent with the selective influence assumption or not. In any case, drift-diffusion modelling revealed differences in the underlying decision processes for reward, task-relevance, and intrinsically relevant images.
To conclude, although earlier eye movement responses or a stronger maintenance of saccadic accuracy can be found with monetary reward, perceptual tasks as well as image content, we here show that these visual consequences differ in terms of their motivational abilities.
Thus, although these consequences might apparently evoke the same behavior, this is not for the same reason. Bieg, H. PLOS ONE, 7 9. Thanks to brain plasticity , the brain is able to change its structure and function. Brain plasticity allows us to create new brain connections and increase the amount of neural circuits, improving functionality.
If neuroscience and studying brain plasticity has shown us anything, it is that the more neural circuits we use, the stronger they will become , which is applicable to processing speed.
CogniFit will help you perform a complete neurocognitive assessment in which we assess your processing speed, and based on your results, provide you with a complete set of personalized cognitive exercises to improve your cognitive processing speed. The cognitive neuropsychological assessment and stimulation program from CogniFit was designed by a team of neurologists and cognitive psychologists who study the processes of synaptic plasticity and neurogenesis.
You only need 15 minutes a day, times a week to stimulate your cognitive abilities and cognitive processes. This program is available online. The different interactive exercises are presented as fun brain games that you can practice on your computer or tablet.
After each session, CogniFit will provide you with a detailed graph with your progress. It has been proven that CogniFit's online exercises help in the creation of new synapses and neural circuits, which make it possible to reorganize and recover function of the most deteriorated cognitive domains.
In a clinical setting, the CogniFit results when interpreted by a qualified healthcare provider , may be used as an aid in determining whether further cognitive evaluation is needed.
CogniFit does not offer any medical diagnosis or treatment of any medical disease or condition. CogniFit products may also be used for research purposes for any range of cognitive related assessments. If used for research purposes, all use of the product must be in compliance with appropriate human subjects' procedures as they exist within the researchers' institution and will be the researcher's obligation.
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Last updated Accuracg 22, Edited and Ibcreases reviewed by Patrick Alban, DC. Written by Resveratrol and immune system Alban. Spees thinking is important, but mental processing sped Increases mental speed and accuracy often Increases mental speed and accuracy valuable than accuracy. Learn 14 ways to help you think faster and more efficiently. This is not necessarily true, but there are still many reasons why thinking faster can be desirable. Diabetes, smoking, high blood pressure, and other vascular risk factors can starve the brain of oxygen and glucoseresulting in slower thinking.
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