Increases mental speed and accuracy -
One effective way to improve selective attention is through mindfulness meditation. Mindfulness meditation involves training the mind to focus on the present moment, without judgment or distraction.
Research has shown that mindfulness meditation can improve signal-detection tasks and enhance selective attention. Some of the top athletes today who utilize meditation as part of their regiment include LeBron James, Barry Zito, Steph Curry, and Pete Carroll who has the entire Seattle Seahawks roster participate in meditation.
During some TV timeouts, you can see LeBron James step away from his team and meditate before returning to play. The NeuroCatch ® radar plot in Figure 1 below is a visual depiction of peer-reviewed cognitive evoked potential data from the effects of meditation training.
Cognitive flexibility is the ability to switch between tasks or mental sets quickly and efficiently. In sports, cognitive flexibility is essential for athletes to adapt to changing game situations and adjust their strategy accordingly.
By training cognitive processing speed, athletes can increase their cognitive flexibility and improve their overall adaptability during competition. One effective way to improve cognitive flexibility is through dual n-back training. Dual n-back training involves challenging athletes to remember and recall increasingly complex sequences of stimuli.
Research has shown that dual n-back training can improve cognitive flexibility and working memory, which can be beneficial for athletes in a variety of sports.
Other interventions for improving cognitive processing includes transcranial photobiomodulation therapy. By incorporating dual n-back training into their training regimen, athletes can develop the cognitive skills needed to adapt quickly to changing situations and improve their performance.
In conclusion, optimizing mental performance through attentional and cognitive processing should be a core pillar in the training regiments of high-performance athletes.
By improving reaction time, enhancing attentional processing and increasing cognitive flexibility, athletes can strive to gain a competitive advantage over their peers by being the better mental performer.
From New York Mets great Pete Alonso to the late great Kobe Bryant whose MAMBA mentality incorporated cognitive processing training , to seven-time Super Bowl Champion Tom Brady who credits training his processing speed for his longevity, the best of the best are leveraging the ability to think and react quicker than their opponents.
An early-mover advantage is also available to athletes who incorporate cognitive training into their core program, as the neurocognitive performance market is relatively untapped by athletes in the present day. By doing so, you can help your athletes reach their full potential and excel in their sport.
This is also called the translator mechanism. In the example of our ball, this means that your brain translates the received information into the possible decision like kicking the ball with your right or left foot. This is also known as the effector mechanism.
The effector mechanism receives the previously made decision and translates it into an executable plan ready to send a plan for the relevant part of your brain. After the plan is defined, the plan will be send in the form of a signal.
In the example of our ball, this means that the part of the brain responsible for moving your body receives the signal. This part of the brain is also called the motor cortex.
The relevant part of the brain receives the signal, resulting in the execution of the desired reaction. Depending on what part of the brain receives the signal, your body might move, you might speak, you might start thinking of a solution for a specific problem, or any other form of reaction.
In the example, this means that the part of the brain that is responsible for the movement of your body receives the signal. The output in our situation is moving to kick the ball with your right foot.
The final stage is receiving feedback. This occurs in the form of signals within your brain telling you if the performed reaction was successful. Your brain judges the outcome of your reaction by comparing it to other experiences that are stored within your memory.
Depending on the outcome of your reaction, you might have kicked the ball in the wrong direction because you used the wrong part of your foot.
A well developed thinking speed, causes you to react faster to situations. This might have a lot of benefits when executing tasks ranging from easy to hard ones. Important skills relevant to perform these tasks such as planning, problem-solving or staying focussed can be enhanced by a quicker thinking speed.
It might benefit the time in which you are trying to study for a school exam, athletic performance during sport or simply your response time when driving.
But it could mean you find it harder or easier to perform tasks such as reading, math, taking notes, listening. It might also affect other cognitive skills and executive functions such as planning, decision making, facial recognition, divided attention, visual perception, mental flexibility or pattern recognition.
Since the memory and attention play a major roll in learning, having a faster thinking speed might lead to greater intelligence. So memory plays an important role in learning, and faster thinking speed could enhance this cognitive skill.
But how does that work? As explained in our article about the memory, the working memory can temporarily store a limited amount of information. This means that if you process information slower, the limited storage capacity of your working memory would quickly fill up.
Resulting in more information being able to be passed through the short-term memory and eventually the long-term memory. Based on preliminary analysis on the Accuracy-groups, we selected the following electrodes on the left AF3-F3-F7-FC5 and right AF4-F4-F8-FC6 prefrontal cortex PFC ; the ERPs recorded at these electrodes were averaged in order to obtain a representative pool of activities in each hemisphere of the PFC.
The mean amplitude between ms before and 50 ms after stimulus onset at the two selected pools was submitted to a 2 × 2 × 2 ANOVA Group × Pool × Condition.
The overall α -level was fixed at 0. Based on the peak electrodes, the typical post-stimulus ERPs components were measured as follows: the P1 on PO8, the N1 on PO7, the N2 on Cz, and the P3 on Pz and Cz in the go and no-go condition, respectively.
The peak amplitude and latency of these components were submitted to separate 2 × 2 ANOVAs with Group Fast vs. Slow or Accurate vs. Inaccurate as between factor and Condition go vs. no-go as within factor. In a study combining EEG and fMRI measures Di Russo et al. The positivity enhancement over the frontopolar derivations was closely associated to the go condition as triggering the response execution Berchicci et al.
In the present study, to better isolate the pP component, we adopted the differential method subtracting the individual no-go ERP from the go ERP of the same subject; the individual subtraction waves were then separately averaged for Speed- and Accuracy-groups.
Obviously, the risk in adopting this method is to indistinctly subtract different activities taking place in the same period. In order to avoid this, we limited our analyses on the Fp1 and Fp2 sites in the time window following the stimulus appearance.
This method was motivated by the fact that we wanted to emphasize the prefrontal positive activity, expecting to find latency modulations as a consequence of difference in response speed. We also looked at that component in the accuracy-groups, in which the speed-match should not produce a modulation in the peak latency.
The visual inspection of the averaged differential waves showed a positive peak at approximately ms bilaterally over the frontopolar electrodes i. The onset latency calculated as the first deflection larger than twice the absolute value of the baseline mean and the peak amplitude and latency of the dpP were submitted to 2 × 2 ANOVAs with Group and Site Fp1, Fp2 as factors, repeated for both Speed- and Accuracy-groups.
Figure 1 illustrates the ERP waveforms of both Speed- Figure 1A and Accuracy-groups Figure 1B at three relevant sites AF4, Cz, PO8 for both go and no-go conditions.
Time 0 represents the stimulus onset; inspection of the figure indicates that these stimulus-locked ERPs using long pre-stimulus analysis allow to appreciate the motor preparation activity, which is usually obtained by the motor response-locked ERPs, called movement-related cortical potentials MRCPs.
Figure 1. Grand averaged waveforms of Speed- A and Accuracy-groups B in the three relevant sites AF4, Cz, PO8 ; time 0 corresponds to the stimulus onset. The different groups and task conditions are superimposed with different colors. pN, prefrontal negativity; BP, Bereitschaftspotential.
No differences were found between go and no-go conditions before stimulus onset. In all groups, the prefrontal negativity pN started about ms before the stimulus appearance see AF4 ; ms later, over Cz, emerged the BP that progressively raised reaching its maximum at about ms before the stimulus onset.
The BP component was larger in the fast than the slow group, while the two Accuracy-groups had identical BP component. By contrast, the pN was modulated by the accuracy only, i. Figure 2A shows the topographical distribution of the aforementioned pre-stimulus activities.
The activity over the medial frontal-central areas likely the SMA in the fast group was larger than the slow group; on the other hand, the inaccurate group showed a greater negativity than the accurate over the PFC, especially in the right-hemisphere.
In order to visually enhance the presence of hemispheric differences in the inaccurate group, Figure 2B shows the differential waves obtained over lateral PFC by subtraction of the grand averaged ERP of accurate group from that of the inaccurate.
Figure 2. A Scalp topographies top-flat view of the grand averaged pre-stimulus activities in the Speed- and Accuracy-groups. Time 0 corresponds to the stimulus onset. These results suggest that: a the larger the BP component, the faster the behavioral response; and b the larger the pN activity especially on the right side , the worst the accuracy performance.
Figure 3. A Pre-stimulus activity. Left side: correlation scatterplot of the RT with the BP amplitude in the Speed-groups. Right side: correlation scatterplot of the FA with both the left and right PFC activity indexed by the pN in the Accuracy-groups. B Post-stimulus activity: means and standard deviations of the main ERPs components.
From the upper left: P1 amplitude in Accuracy-groups; N1 amplitude in Speed-groups; N2 amplitude in Speed-groups; N2 amplitude in Accuracy-groups; P3 amplitude in Speed-groups; P3 latency in Speed-groups.
At about ms emerged the N2 peaking on medial frontal sites Cz. Finally, the P3 component peaked between and ms over medial parietal and frontal sites.
Statistical comparisons of the aforementioned components are shown in Figure 3B. In other words, larger N2 components were associated with faster RTs and more errors in both groups. In the Accuracy-groups the effects on the P3 were not significant.
These data indicate that faster responses were associated with earlier and larger P3 peaks. The results of the correlation analyses between electrophysiological data in the Speed- and Accuracy-groups are reported in Table 2.
Overall, accuracy modulated the P1 and the N2 components in two opposite ways. The more accurate performance correlated with larger P1 amplitude and smaller N2 amplitudes. Speed modulated the N1, N2 and P3 components; the larger their amplitudes, the faster the RTs. For the P3 component, also the latency was related to RTs speed the shorter P3 latency, the faster RTs.
Table 2. Correlations r -values between ERP components in the Speed- and Accuracy-groups. To enhance the go-related pP, the differential waves go minus no-go were calculated on the frontopolar derivations Fp1, Fp2 , limiting the analyses to the time window following the stimulus.
By this method, the no-go condition acted as baseline for the go ERP in each subject: this procedure was motivated by the fact that the pP activity was closely associated to the response trials i.
Figure 4 shows the difference waveforms restricted to the post-stimulus period over the left prefrontal site Fp1 , in which the dpP was largely pronounced.
In the Speed-groups, the dpP of the fast group started approximately 60 ms earlier than the slow group, and this difference partially remains until the peak, which was reached at and ms by the fast and slow group, respectively.
Furthermore, the peak was larger in the fast than slow group. On the other hand, the accurate group had larger dpP than the inaccurate group, but latency differences were not present.
Figure 4. Go minus no-go difference wave: the differential prefrontal positivity dpP. Differential activity is reported for the left frontopolar electrode Fp1 for both Speed- top and Accuracy-groups bottom.
Time 0 represents stimulus onset. Overall, this differential wave enhancing go-related processing at prefrontal level was a sensitive marker of the efficiency of the decision processing in both Speed- and Accuracy-groups.
This study aimed at identifying the neural processing stages associated with the SAT using a novel approach, i. Moreover, we recorded the frontal activity with a much more dense electrode array than previous electrophysiological studies Osman et al.
Finally, we considered the characteristics of ERP components after stimulus, highlighting different levels of perceptual processing associated with response speed or response accuracy.
The anticipatory brain activities the BP and pN components showed group differences depending on the speed or the accuracy of the subsequent motor response. Fast and slow groups matched in accuracy had different BP amplitudes and similar pN amplitudes; at the opposite, accurate and inaccurate groups matched in speed had different pN amplitudes and similar BP amplitudes.
The sources of these components were located in different areas of the frontal cortex: the SMA for the BP component Di Russo et al. An enhanced SMA activity in the last half second before the stimulus onset characterized subjects with fast responses with respect to slow subjects.
By contrast, an enhanced rPFC activity starting ms before the stimulus onset characterized inaccurate subjects with respect to very accurate subjects. Correlations between SMA amplitude and RTs on one side, and between rPFC amplitude and accuracy on the other further support the different roles played by these two frontal areas into speed and accuracy processing.
However, it is noteworthy that the pre-stimulus activities were correlated the larger the BP, the larger the pN within both Speed and Accuracy groups, pointing to a stable relationship between SMA and rPFC activity.
The enhanced SMA activity was associated with speed instructions in fMRI Forstmann et al. Neurophysiologically, larger SMA activity under speed constrain might contribute to overcome the tonic inhibition provided by the output nuclei of basal ganglia Lo and Wang, Present findings showed that the subjects with a spontaneous tendency to be fast had an enhanced SMA activity starting ms before the stimulus onset, suggesting that baseline activity increased in fast performers.
Indeed, a reduced baseline-to-threshold distance could account for the shorter time needed to reach a motor response Bogacz et al.
On the other hand, it is still a matter of debate the role played by prefrontal areas in the SAT processing, although the engagement of the rPFC in the response accuracy is supported by studies on the response inhibition Garavan et al.
Present findings indicate that the rPFC activity starting ms before the stimulus onset was accuracy-related larger in the inaccurate than accurate group.
Thus, two interacting but separate neurocognitive systems may represent the basis of the individual tendencies underlying the baseline modulation of different baseline-to-threshold systems. Thus, we propose that SAT is the result of the co-activation of the two interacting systems.
Indeed, considering the anatomo-functional connections between the SMA and rPFC for a review see Aron, , it could be proposed that an increased baseline activity in the SMA-rPFC network leads to fast and inaccurate performance, while the decreased baseline accounts for the trade-off in the sense of slow and accurate responses.
Data on post-stimulus activities are consistent with the view that accuracy- or speed-related individual tendency might affect also the activity of visual cortical areas.
We observed a dissociation of the two visual components P1 and N1, which had larger amplitudes in the accurate and fast groups than slow and inaccurate groups, respectively. The dissociation was further confirmed by the correlation analyses, showing that larger P1 amplitude was associated with high accuracy, and larger N1 amplitude was associated with high speed.
A vast literature showed that spatial attention produces an amplification of stimulus-evoked activity in extrastriate areas and posterior parietal cortex PPC during the 80— ms following the stimulus onset Luck et al. The P1 component enhancement represents facilitation at the early sensory processing level for items presented at attended location Di Russo et al.
In addition to the modulation of the extrastriate areas, visual attention control relies on a network of cortical and subcortical regions, including the DLPFC and PPC, the anterior cingulate gyrus, and the pulvinar nucleus of the thalamus Mesulam, ; Nobre et al.
Thus, it is likely that the modulations of the visual areas observed in the present study are part of a perceptual decision-making process, starting with pre-stimulus baseline adjustments and ending up with the response threshold reaching.
Support to this hypothesis comes from a single cell recording Heitz and Schall, showing that the SAT-related cues induced a shift of baseline firing rates in the visually responsive neurons of the frontal eye field FEF. At the same time, under the framework of the drift-diffusion models, recent studies Rae et al.
These latter hypotheses are consistent with the present findings, pointing to a greater allocation of visual-spatial attention in the accurate group, as revealed by the P1 amplitude. Further studies are needed to shed light into the brain networks underlying the speed- and accuracy-oriented perceptual processes, as indexed by the P1-N1 modulation.
The N2 modulation is generally described as an index of inhibitory control e. However, we will not discuss the N2 data in these terms, because in a recent study Di Russo et al. Further studies are required to clarify this issue, which is outside the scope of present work.
The P3 component, usually described as an index of the stimulus categorization process Mecklinger and Ullsperger, , started earlier and was larger in the fast than slow group, whereas no differences emerged between accurate and inaccurate groups.
The correlation analyses further confirmed the relationship between the RT and the P3 component, suggesting that the P3 could also provide an estimation of the stimulus evaluation time that is closely related to the response processing time.
Finally, are crucial the effects found on the prefrontal pP. We confirmed that this newly discovered components, compared to no-go, is larger in the go condition as previously described by our group Di Russo et al. The neural generator of the pP was localized in the anterior Insula in a study combining fMRI and ERP data collected with the same task used in the present study Di Russo et al.
In the present study, we additionally adopted the subtraction method to better focus on the pP modulation on prefrontal sites: the main risk of this procedure is to compare different activities acting in the same period.
For this reason, our analyses and interpretation were limited to the differential activity resulting from the frontopolar derivations in the time window following the stimulus. In line with our predictions, we observed a positive component, called dpP, peaking at about ms after the stimulus: thus, differential analyses further confirmed the presence of a positive activity closely related to the response execution, as previously observed in other studies Di Russo et al.
Taking into account these views and the present data, we suggest that the dpP might reflect the S-R mapping finalized to the response execution in a perceptual discrimination task, representing the final stage of the decision process before the movement onset.
Analyses on the dpP showed that the latency of this differential wave reflects the speed of the decision-making processing. Moreover, the dpP wave was larger in both fast and accurate groups than their respective counterparts, suggesting that its amplitude reflects the efficiency of the decision process in both cases.
In summary, present results showed different brain activities both before and after stimulus onset in Speed- and Accuracy-groups. Pre-stimulus activity in the SMA and rPFC seems to reflect the baseline modulation of the speed and accuracy decision systems: they are interacting, as revealed by present analyses and anatomo-functional connections between SMA and rPFC for a review see Aron, Thus, we suggest that the typical trade-off between response speed and accuracy is accounted by the baseline activity in the SMA-rPFC network.
A baseline increase in this network could prepare subjects to fast and inaccurate performance, while a reduced baseline may predict slow and accurate performance because of the greater baseline-to-threshold distance in both the speed and accuracy systems.
In addition, we showed that the speed and accuracy baselines can also be separately modulated, leading to either high or low group performance in one system without affecting or affecting very little the other, as indicated by comparable mean performance in the other system. Thus, as previously suggested, the two systems should be considered interacting but not totally dependent.
Finally, after stimulus onset, ERP components reflecting perceptual processing, S-R mapping and stimulus categorization were also differentially affected by speed and accuracy idiosyncratic tendencies.
Overall, the present study suggests that the motor response in a perceptual discrimination task should be considered as the final output of a series of neurocognitive processes starting long before the stimulus onset.
Obviously, all brain areas were active in both speed and accuracy processing; however, some areas were more involved in the speed with respect to accuracy system, and we tried to distinguish them by using different colors. Before stimulus onset the baseline activity of the speed and accuracy systems was modulated by the SMA reflected by the BP and the rPFC reflected by the pN , respectively.
Even if the activity of these prefrontal areas was correlated accounting for the interaction between the two systems , the larger SMA activity marked only the fast group, while the larger rPFC activity marked only the inaccurate group.
About ms after the stimulus onset, the early sensory processing of the extrastriate areas P1 component was modulated by the accuracy level, with the accurate group focusing greater attention to the attended location. Immediately after, extrastriate visual and parietal areas N1 component showed a more intense processing, likely corresponding to the discrimination stage, in the fast than the slow group.
Because of this enhanced sensory processing, the response-oriented S-R mapping in the anterior Insula as reflected by the dpP was reached earlier and Insula activity was larger in fast with respect to slow group; moreover, also accuracy affected the anterior Insula activity larger dpP in the accurate than inaccurate group , although its activity was not directly correlated with the rPFC modulation.
In a time window around ms, the activity corresponding to the stimulus categorization in the PPC P3 component and response execution in the case of go stimuli, was especially affected by response speed.
Figure 5. Sketch of the processing in the preparation-perception- action cycle and associated brain areas as a function of time not scaled. Obviously the same brain areas were involved in both speed- orange and accuracy blue - processing; however, the activity of some areas was more affected by either one or the other condition: the orange and blue lines depict the two main flows within speed and accuracy systems.
In Performance optimization plugins competitive sports environment, athletes are always looking for an edge over their opponents. Many high-performance Muscle development variations spend countless hours training their wnd abilities, menta often overlook the importance of training their meental abilities. Increaees physical training is Increases mental speed and accuracy and relatively easy to measure objectively, many athletes overlook the importance of training their cognitive abilities, perhaps because of the inability to quantify progress. In this blog post, we will discuss why training cognitive processing speed is a must-have for high-performance athletes, and how objectively measuring it opens new avenues to gain a competitive edge. Historically, reaction time has been viewed as critical for athletes to succeed in their sport. Classical understanding of reaction time involves the speed-accuracy trade-off.Visual selection is characterized by a trade-off between speed and Shop Amazon Online. Speed or accuracy of the selection process can be affected by higher level factors—for example, expecting a reward, obtaining task-relevant information, or seeing an intrinsically relevant target.
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Changes in decision threshold are thus typically indicative of a snd trade-off. A recent study showed that motivation accuraxy monetary reward can operate Increasss the speed—accuracy trade-off by simultaneously metal up responses and Muscle development variations response accuracy Manohar et al.
In that study, participants fixated one of three Increades in a triangular Increades. A recorded voice provided information about the maximum amount of reward that could be obtained in that trial.
Sleed, the Incraeses two Increases mental speed and accuracy changed their luminance one after the other. The disc lit first was the distractor and had to be ignored, whereas the disc lit second was the target and had to be selected by means of a saccadic eye movement.
Participants received monetary reward if they correctly selected the target disc. Importantly, the obtained reward decreased with increasing reaction time such that a mature response might only yield a small fraction of the announced maximum reward. The authors found that motivation by monetary reward decreased both reaction times and error rates, inconsistent with a speed—accuracy trade-off.
The results were explained by a model that included a noise-reduction component that operates perpendicular to the speed—accuracy trade-off, but which comes at a cost.
This precision cost can explain why motivation by reward can increase both speed and accuracy. Earlier, faster or more accurate saccades have not only been reported in studies employing a monetary reward Chen et al. Yet a benefit in either speed or accuracy does not necessarily imply that obtaining task-relevant information or seeing an intrinsically relevant image affects selection processes via motivational mechanisms and, furthermore, that it can reduce internal noise and simultaneously increase speed and accuracy.
To test this, we adopted the paradigm introduced by Manohar et al. Across different blocks, participants either obtained a monetary reward, obtained task-relevant information for a perceptual task, or saw the face of a famous person.
We compared speed and accuracy from these trials relevant trials with interleaved trials from the same block, lacking these consequences irrelevant trials. At the beginning of each trial, participants were informed whether a trial was relevant e. This enables attributing differences in speed and accuracy to motivational processes.
We complement our analysis with a drift-diffusion modelling approach to attribute the observed differences in speed and accuracy to differences in decision threshold and information uptake. In addition, participants received a performance-dependent monetary reward of up to 9.
Obtained rewards were rounded up to the first decimal after the comma and ranged from 2. Written informed consent was provided before testing. The experiment was approved by the ethics committee of the Department of Psychology and Sport Sciences of the University of Münster.
Stimuli were presented on an Eizo FlexScan inch CRT monitor Eizo, Hakusan, Japan with a resolution of 1, × pixels, a refresh rate of 75 Hz, and an effective display size of Participants viewed stimuli from a 67 cm distance.
Head movements were restricted by means of a chin—forehead rest. Stimulus presentation was controlled via the Psychtoolbox Brainard, ; Kleiner et al. Eye position of the right eye was recorded at Hz using the EyeLink SR Research, Mississauga, ON, Canada and the EyeLink Toolbox Cornelissen et al.
All stimuli were presented on a black background. The EyeLink was calibrated at the beginning of each block using a 9-point calibration protocol. The experiment comprised three blocks reward, task, face. Each block contained trials and consisted of two trial types that differed in terms of the consequences of a successful saccade.
We refer to these two trial types as relevant and irrelevant trials. In relevant trials, participants either received a small monetary reward of up to 0.
In irrelevant trials, participants received no monetary reward reward blockperformed no perceptual task task blockor saw a grating face block. All blocks were recorded within one session of 60—90 minutes with breaks in-between blocks.
The order of blocks was balanced across participants. At trial beginning, a centrally displayed text, shown for 1s Fig. Afterwards, a central white fixation cross and four dark blue discs appeared. Discs had a radius of 2 deg and were arranged in a deg square pattern around the fixation point.
Thus, the total distance between the fixation cross and each disc was approximately After a uniform random interval between 0. After additional ms, the target disc changed to gray. Target and distractor were either horizontally or vertically adjacent and were therefore always separated by 16 deg.
Consequently, there were two potential target discs for every distractor location. A disc was labelled as selected by a gaze movement if gaze was less than 4 deg away from a disc center.
All targets were removed ms after disc selection or ms after target onset. Participants were instructed to look at the target. No instruction towards speed or accuracy was given.
Trial procedure and critical manipulation. a Trial procedure common for all three blocks. Four blue discs appeared, of which two changed successively. Participants had to look at the disc changing second target while ignoring the disc changing first distractor.
Given that distractor and target were always next to each other, knowing the distractor location renders two discs possible target locations: The actual target and the disc opposing the target opposing disc. b Consequences following successful target selection in relevant trials of the three respective blocks.
Participants either received a monetary reward, saw a face of a famous person, or had to perform a perceptual task at the saccade target. c Reward, image quality and tilt decayed with increasing reaction time. Thus, later responses came along with a diminished reward, worse image quality and thus impaired recognition or a more difficult perceptual task.
The actual decay in each trial was derived from an exponential decay function. The black line denotes the decay function as it was set at the beginning of each block.
The decay additionally depended on the median latency of the previous trials to assure a constant difficulty across the experiment and across participants.
The initial decay corresponded to a median latency of ms dashed black lines. The thin gray lines show how the decay would have become steeper or shallower, if the median decreased or increased by ms. Color figure online. Blocks differed with respect to the consequences following a successful saccade target selection Fig.
In the reward block, if the trial was relevant and participants selected the correct disc, they received a small monetary reward. The maximal reward in each trial was 0. Feedback e. In the task block, a grating Gabor patch was displayed on the target disc upon selection.
The grating had a spatial frequency of 1. The gratings orientation slightly deviated clockwise or counterclockwise from vertical. The maximum tilt was 4 deg and decreased with increasing reaction time, making the perceptual task more difficult the slower the response see Decay, below.
After stimuli removal, a white bar appeared that was tilted clockwise or counterclockwise by 4 deg relative to vertical. Using two different buttons on a keyboard, participants could alternate between these two response options and select the option that they thought corresponds to the tilt direction of the grating.
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| How your brains thinking speed works | In line Nutritious post-workout meals our predictions, we observed a positive component, called dpP, accurcy at about accuuracy after the Nutritious post-workout meals thus, differential analyses Muscle development variations confirmed menntal presence of Organic farming techniques positive wccuracy Muscle development variations related to the response execution, as previously observed in other studies Di Russo et al. Toxic Femininity, Explained — Plus, Tips to Overcome This Mindset Toxic femininity, or behavior that aligns with patriarchal beliefs about what women should and shouldn't do, can affect your well-being. Brain plasticity allows us to create new brain connections and increase the amount of neural circuits, improving functionality. Key results on BrainHQ include:. Drink water. |
| Rewires the Brain | A good clue that a medication is anticholinergic is in the common name of its general classification. If you suspect that any of your medications are affecting your thinking, look for them on this list of common anticholinergic medications. Thinking is slower in childhood, then ramps up during the teens and young adulthood, stabilizes during middle age, and ultimately slows down during the senior years. A clinical study on twins found that genes play a large role in mental processing speed as well. Processing speed is defined as the time it takes the brain to take in new information, reach some judgment on it, and then formulate a response. For most people, the efficiency and accuracy of thought, rather than speed, are the limiting factors. Gary L. Wenk, PhD, professor of psychology and neuroscience, is a leading authority on the consequences of chronic inflammation on the brain. In Psychology Today , Dr. Wenk writes:. Most of us can push the processing speed a little without risk. Unfortunately, the neural processing speed in our brains is already just a few extra action potentials per second away from a full-blown seizure. So, perhaps the more important goal is to learn how to think more efficiently and accurately, rather than simply faster. Fast thinkers are more likely to engage in risky behaviors than those who think more deliberately and slowly. Here are some of the best ways to make thinking not just faster, but also more efficient and accurate as well. When you really want to make a decision fast, flip a coin. You may find yourself inwardly hoping for one outcome over another which will help you know what you really want. If you do puzzles like crossword or Sudoku or play games like chess, set a timer to force yourself to work faster. The reason? All of us confront multiple nutrient thieves — stress, poor diet, insomnia, pharmaceuticals, pollution, and more — that steal nutrients that the brain needs to thrive. A foundational principle of mental health and cognitive performance is to supply the body with the best nutrition possible. And, when you buy a 3-month supply of any Performance Lab supplement, you get 1 extra month free. See why I recommend Performance Lab. Getting adequate quality sleep is one of the most important things to do for optimal brain function. Lack of sleep negatively impacts both thinking speed and accuracy. Even moderate sleep loss can affect your mental performance as much as being drunk! The ideal temperature for optimal thinking seems to be around 72 °F 22 °C. A regular meditation practice builds a more efficient brain by stimulating the formation of new brain cells and neural connections, and by increasing brain plasticity. Meditation strengthens communication between brain cells which, in turn, speeds up mental processing , enhances the capacity to learn, and improves the ability to concentrate. Musicians have bigger, better connected, more symmetrical brains. Having musical training improves processing speed , cognitive skills , and working memory. The Advanced Cognitive Training for Independent and Vital Elderly ACTIVE study was the first large-scale trial to show that computerized brain training can improve cognitive function in older adults. This study concluded that computerized brain training provided long-lasting improvements in memory, reasoning, and processing speed. Another way to challenge your mental speed is by taking a reputable timed intelligence test, such as the Mensa IQ Challenge. But anyone can exercise their brain with their free Mensa IQ Challenge. Take this question test when you have a bit of uninterrupted time; it has a minute time limit. Engaging with others face-to-face in real time, rather than digitally, will force you to think faster. One huge study on over 1 million men found that exercise can actually raise IQ. The next time you interact with someone, take note of four things about them. Maybe you observe the color of their shirt or pants. Are they wearing glasses? Do they have a hat on, and if so, what kind of hat? What color is their hair? Once you decide on four things to remember, make a mental note, and come back to it later in the day. Write down what you remember about those four details. Focusing on your brain health is one of the best things you can do to improve your concentration, focus, memory, and mental agility, no matter what age you are. Our experts continually monitor the health and wellness space, and we update our articles when new information becomes available. Practicing certain lifestyle habits may help boost your intelligence and stimulate your brain. Research has shown that when done regularly, these…. Constantly dream of romance? Fixate on thoughts of your partner? Feel a need to always be in love? Learn why — and why this isn't an "addiction. Toxic femininity, or behavior that aligns with patriarchal beliefs about what women should and shouldn't do, can affect your well-being. Here's how. A new study, released this week has found that death rates are increased for people with obesity who are also socially isolated and lonely. A new study finds a type of psychedelic called ibogaine may help people with traumatic brain injury. In the study 30 male Special Operations Forces…. New research suggests that moderate-intensity aerobic exercise like swimming, cycling, jogging, and dancing may be more effective for reducing…. Finding a therapist that makes you feel comfortable is crucial. But that's not the only consideration. Here's what else to look for when starting a…. A Quiz for Teens Are You a Workaholic? How Well Do You Sleep? Health Conditions Discover Plan Connect. Mental Well-Being. Medically reviewed by Timothy J. Legg, PhD, PsyD — By Sara Lindberg — Updated on February 17, Try puzzles Play cards Build vocabulary Dance Use your senses Learn a new skill Teach a skill Listen to music Try a new route Meditate Learn a new language Do tai chi Focus Bottom line Exercising the brain to improve memory, focus, or daily functionality is a top priority for many older adults. Share on Pinterest. Brain exercises. Have fun with a jigsaw puzzle. Try your hand at cards. Build your vocabulary. Conversely, if we prioritize speed, our behavior is sometimes premature and performance will be prone to errors. The speed—accuracy trade-off describes this lawful relationship between speed and accuracy for a fixed task difficulty Fitts, ; Heitz, ; Standage et al. Speed—accuracy data are often modelled using sequential sampling models for reviews see Bogacz et al. Sequential sampling models commonly assume that information from the environment is constantly sampled until a threshold is reached and a response is carried out. A clear advantage of the drift-diffusion model and other sequential sampling models is that the joint modelling of speed and accuracy allows one to infer latent psychological variables like response bias, information uptake, and decision threshold Voss et al. The latter is reflected in the drift rate parameter. Decision threshold on the other hand is reflected in the boundary separation parameter and can be affected when either speed or accuracy is emphasized. Changes in decision threshold are thus typically indicative of a speed—accuracy trade-off. A recent study showed that motivation by monetary reward can operate outside the speed—accuracy trade-off by simultaneously speeding up responses and increasing response accuracy Manohar et al. In that study, participants fixated one of three discs in a triangular arrangement. A recorded voice provided information about the maximum amount of reward that could be obtained in that trial. Then, the other two discs changed their luminance one after the other. The disc lit first was the distractor and had to be ignored, whereas the disc lit second was the target and had to be selected by means of a saccadic eye movement. Participants received monetary reward if they correctly selected the target disc. Importantly, the obtained reward decreased with increasing reaction time such that a mature response might only yield a small fraction of the announced maximum reward. The authors found that motivation by monetary reward decreased both reaction times and error rates, inconsistent with a speed—accuracy trade-off. The results were explained by a model that included a noise-reduction component that operates perpendicular to the speed—accuracy trade-off, but which comes at a cost. This precision cost can explain why motivation by reward can increase both speed and accuracy. Earlier, faster or more accurate saccades have not only been reported in studies employing a monetary reward Chen et al. Yet a benefit in either speed or accuracy does not necessarily imply that obtaining task-relevant information or seeing an intrinsically relevant image affects selection processes via motivational mechanisms and, furthermore, that it can reduce internal noise and simultaneously increase speed and accuracy. To test this, we adopted the paradigm introduced by Manohar et al. Across different blocks, participants either obtained a monetary reward, obtained task-relevant information for a perceptual task, or saw the face of a famous person. We compared speed and accuracy from these trials relevant trials with interleaved trials from the same block, lacking these consequences irrelevant trials. At the beginning of each trial, participants were informed whether a trial was relevant e. This enables attributing differences in speed and accuracy to motivational processes. We complement our analysis with a drift-diffusion modelling approach to attribute the observed differences in speed and accuracy to differences in decision threshold and information uptake. In addition, participants received a performance-dependent monetary reward of up to 9. Obtained rewards were rounded up to the first decimal after the comma and ranged from 2. Written informed consent was provided before testing. The experiment was approved by the ethics committee of the Department of Psychology and Sport Sciences of the University of Münster. Stimuli were presented on an Eizo FlexScan inch CRT monitor Eizo, Hakusan, Japan with a resolution of 1, × pixels, a refresh rate of 75 Hz, and an effective display size of Participants viewed stimuli from a 67 cm distance. Head movements were restricted by means of a chin—forehead rest. Stimulus presentation was controlled via the Psychtoolbox Brainard, ; Kleiner et al. Eye position of the right eye was recorded at Hz using the EyeLink SR Research, Mississauga, ON, Canada and the EyeLink Toolbox Cornelissen et al. All stimuli were presented on a black background. The EyeLink was calibrated at the beginning of each block using a 9-point calibration protocol. The experiment comprised three blocks reward, task, face. Each block contained trials and consisted of two trial types that differed in terms of the consequences of a successful saccade. We refer to these two trial types as relevant and irrelevant trials. In relevant trials, participants either received a small monetary reward of up to 0. In irrelevant trials, participants received no monetary reward reward block , performed no perceptual task task block , or saw a grating face block. All blocks were recorded within one session of 60—90 minutes with breaks in-between blocks. The order of blocks was balanced across participants. At trial beginning, a centrally displayed text, shown for 1s Fig. Afterwards, a central white fixation cross and four dark blue discs appeared. Discs had a radius of 2 deg and were arranged in a deg square pattern around the fixation point. Thus, the total distance between the fixation cross and each disc was approximately After a uniform random interval between 0. After additional ms, the target disc changed to gray. Target and distractor were either horizontally or vertically adjacent and were therefore always separated by 16 deg. Consequently, there were two potential target discs for every distractor location. A disc was labelled as selected by a gaze movement if gaze was less than 4 deg away from a disc center. All targets were removed ms after disc selection or ms after target onset. Participants were instructed to look at the target. No instruction towards speed or accuracy was given. Trial procedure and critical manipulation. a Trial procedure common for all three blocks. Four blue discs appeared, of which two changed successively. Participants had to look at the disc changing second target while ignoring the disc changing first distractor. Given that distractor and target were always next to each other, knowing the distractor location renders two discs possible target locations: The actual target and the disc opposing the target opposing disc. b Consequences following successful target selection in relevant trials of the three respective blocks. Participants either received a monetary reward, saw a face of a famous person, or had to perform a perceptual task at the saccade target. c Reward, image quality and tilt decayed with increasing reaction time. Thus, later responses came along with a diminished reward, worse image quality and thus impaired recognition or a more difficult perceptual task. The actual decay in each trial was derived from an exponential decay function. The black line denotes the decay function as it was set at the beginning of each block. The decay additionally depended on the median latency of the previous trials to assure a constant difficulty across the experiment and across participants. The initial decay corresponded to a median latency of ms dashed black lines. The thin gray lines show how the decay would have become steeper or shallower, if the median decreased or increased by ms. Color figure online. Blocks differed with respect to the consequences following a successful saccade target selection Fig. In the reward block, if the trial was relevant and participants selected the correct disc, they received a small monetary reward. The maximal reward in each trial was 0. Feedback e. In the task block, a grating Gabor patch was displayed on the target disc upon selection. The grating had a spatial frequency of 1. The gratings orientation slightly deviated clockwise or counterclockwise from vertical. The maximum tilt was 4 deg and decreased with increasing reaction time, making the perceptual task more difficult the slower the response see Decay, below. After stimuli removal, a white bar appeared that was tilted clockwise or counterclockwise by 4 deg relative to vertical. Using two different buttons on a keyboard, participants could alternate between these two response options and select the option that they thought corresponds to the tilt direction of the grating. They received one score point for a correct response. Feedback about the current score and overall score e. If participants selected the wrong disc, no grating was displayed. A response bar appeared nonetheless, forcing participants to guess. In irrelevant trials, no response bar appeared and thus no feedback was displayed. In relevant trials of the face block, the face of a well-known person was displayed centered on the target disc once the target disc was selected. Gray-scale images were circular with a diameter of 2. We selected images from the internet with a frontal perspective and with a neutral or smiling facial expression. The 96 faces used in the experiment were randomly selected from a set of images males, females. The depicted persons included German and international actors, musicians, show masters and politicians. The reason for using images of well-known people was the possibility to recognize a person. Image quality depended on the reaction time in the respective trial such that images degraded with increasing reaction time see Decay, below. To this end, the images were a weighted average between the face images and a noise image in which every pixel was randomly assigned a value ranging from black to white. The relative weight given to the noise image increased with increasing reaction time. In irrelevant trials, a Gabor patch with a horizontal orientation appeared at the target disc upon selection. No noise manipulation was applied. No stimulus appeared if participants selected the wrong disc. In relevant trials, reward, tilt angle or image quality decayed with increasing reaction time Fig. An exponential function yielded a decay factor y for any latency t. Decay factors were values between 0 and 1 and were multiplied with the maximal reward 0. The underlying decay function was the same in all three blocks and can be described as:. The t min parameter was fixed at ms. Thus, reward, tilt angle and image quality started to decay for latencies above ms. The strength of the decay depended on the f parameter, which was determined by the mean latency in the last eight trials. This assured a comparable difficulty throughout the experiment and across participants. At block beginning, f was set to 1. Mean latencies and f values were linearly related Fig. For example, the f parameter was set to 0. Participants were informed about the decay before the experiment. Saccade onsets were defined using the EyeLink algorithm. We compared mean latencies of correct trials using a 3 × 2 repeated-measures analysis of variance ANOVA , with the factors block reward, task, face and relevance relevant, irrelevant. Latency differences between relevant and irrelevant trials within a block were compared using nonparametric Wilcoxon tests. To analyze accuracy time courses, we used the SMART procedure smoothing method for the analysis of response time courses; van Leeuwen et al. Data were analyzed at a 1-ms resolution. We smoothed the data with a Gaussian kernel of ms width and used 1, permutations for every test. Time courses were compared in a time window between 0 and ms after target onset unless noted otherwise. We report four values for every comparison: the p value, the cluster strength of the observed data t , the time window of the strongest cluster in the observed data, and the 95 th percentile of the distribution of cluster strengths that result from permutation. The latter is the critical t value t crit to which the cluster strength of the observed data is compared. The p value is given by the relative position i. Cluster-permutation tests thus replace multiple comparisons e. Hence, the cluster strength t is given by the sum of all t values within the cluster. This cluster test statistic is compared with a distribution of cluster strengths that was obtained from permutation: first, the labels assigning trials to conditions are randomly perturbed. Second, the strongest cluster is determined for the perturbed data. Third, steps one and two are repeated multiple times. We measured saccade deviation away from the distractor as an index of distractor suppression. If the target appeared, for example, at the upper right disc, a distractor at the upper left disc would be considered a counterclockwise distractor, whereas a distractor at the lower right disc would be considered a clockwise distractor. For salient distractors that appear at the same time as the target, early responses deviate towards the distractor. Thus, end points are biased towards the distractor and saccade curve towards it. The opposite can be observed long-latency saccades. Typically, the transition from deviation towards to deviation away can be observed with latencies of around ms McSorley et al. Yet in our paradigm, the distractor preceded the target by ms, and deviation away can be observed even for early reaction times. We therefore analyzed saccades with a reaction time between 80 and ms. Furthermore, for this analysis we only considered correct trials where i gaze was less than 2 deg away from the fixation cross at saccade onset, ii less than 4 deg away from target center at saccade offset disc radius was 2 deg and iii where the gaze shift from fixation cross to target was achieved by a single saccade. Saccades with missing data due to blinks were discarded. In total, Saccade trajectories were first coded relative to the gaze position at saccade beginning. In a second step, trajectories were rotated to correspond to a rightward saccade. Specifically, trajectories were rotated by deg if the target was at the upper right disc, deg for targets at the upper left, deg for the lower left and 45 deg for targets at the lower right disc. In a third step, we normalized the saccade duration to have the same amount of data points for each saccade. To this end, we sampled each trajectory at 25 time points using linear interpolation. In a fourth step, we computed the area under the saccade trajectory as an index of deviation Fig. Deviation indices were compared using a 3 × 2 repeated-measures ANOVA, with the factors block reward, task, face and relevance relevant, irrelevant. The direction of main effects was compared using Bonferroni corrected post hoc t tests. One participant was discarded from the ANOVA, because this participant had less than five trials available in one of the six conditions. We additionally compared deviation index time courses using the SMART procedure with a Gaussian kernel of ms width. We coded the data such that the upper threshold was associated with the correct response and the lower baseline with any error. The model comprised the four main parameters boundary separation, starting point, drift rate, and nondecision time a , z , v , t0 as well as variability parameters of the latter three sz , sv , st0. Drift rate, boundary separation and nondecision time were allowed to vary across the six conditions. Although the selective influence of latent variables on a single model parameter has been challenged e. However, we additionally analyzed nondecision times as a function of the experimental conditions because accuracy instructions have been shown to also affect nondecision time parameters Dutilh et al. The starting point is typically affected by prior information e. A bias may occur if the target appeared more frequently at one location, or if the reward had been larger for a particular location. For reasons of parsimony, we therefore decided to not vary the starting point parameter across conditions, because the location of stimuli was fully balanced, and participants had no prior information that would bias their responses towards any location. The drift rate is normally distributed with mean v and standard deviation sv , whereas the variability of starting point and nondecision time follow a uniform distribution with means z and t0 and width sz and st0 Voss et al. Boundary separation, nondecision time and drift rate parameters were compared using a 3 × 2 repeated-measures ANOVA, with the factors block reward, task, face and relevance relevant, irrelevant. To distinguish whether motivation by reward affects target facilitation or distractor suppression, we conducted Experiment 2, where the distractor was absent in half of the trials. We recorded data of 36 participants age range: 18—29 years, 30 females. The experiment consisted of one block of trials. Each block contained the same number of trials with and without distractor as well as the same number of trials with and without reward 2 × 2 design. Four blue discs appeared. After a uniform random time interval between and 1, ms one of the discs turned white. This was the distractor. After additional ms, the target disc turned gray. Unlike Experiment 1, distractor and target were spatially independent. Thus, the target could appear opposite to the distractor, at one of the two neighboring locations or at the same location, in which case it replaced the distractor. Importantly, for the analysis we only considered trials in which distractor and target location did not coincide. In trials without distractor, the target disc turned gray after the uniform random interval between and 1, ms. The different trial types were randomly interleaved. Saccade latencies were compared using a 2 × 2 repeated-measures ANOVA with the factors distractor presence present vs. absent and reward reward vs. no reward. The analysis of accuracy time courses was equivalent to the main experiment, except that the analysis was restricted to a time window between 80 to ms after target onset in the distractor absent condition, and between 0 and ms after target onset in the distractor present condition. We computed the deviation index for every saccade, consistent with Experiment 1. Thus, for this analysis we only considered distractor-present trials where the distractor was at a neighboring position clockwise or counterclockwise. Deviation indices in rewarded and unrewarded trials were compared using a paired t test. One participant was not considered for this comparison because of less than five trials in one condition. Time courses of deviation indices were compared using the SMART procedure in a time window between 80 and ms using a Gaussian kernel with a width of 32 ms. To analyze how the three different consequences affect the speed and accuracy of target selection, we analyzed saccade latencies of correct responses as an index of speed, and the proportion of trials in which the correct disc was selected as an index of accuracy. Figure 2 shows latencies and their variability in the three different blocks and the two levels of relevance, respectively. Thus, we found faster responses when obtaining monetary rewards or task-relevant information. However, seeing a face versus seeing an irrelevant grating did not affect saccade latencies. Data points gray dots in the left panel denote the mean of an individual, horizontal black lines denote the mean across all participants. Green colors are relevant trials reward, task, seeing a face whereas blueish colors are irrelevant trials no reward, no task, seeing a grating. 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. |
| Brain exercises: 22 ways to improve memory, cognition, and creativity | A accurscy clue mehtal a medication is anticholinergic Increaess in the common name of its general classification. If the target appeared, for cacuracy, at the upper Nutritional periodization principles disc, a distractor Nutritious post-workout meals the upper left disc would be considered a counterclockwise distractor, whereas a distractor at the lower right disc would be considered a clockwise distractor. Yoga also emphasizes breathing from the diaphragm, which helps maximize our oxygen intake, thus improving mental function. You are going to create a company management account. e Comparison of merged data from relevant and irrelevant trials i. Key results on BrainHQ include:. |
| Processing Speed | Heekeren, H. Gray-scale images were circular with a diameter of 2. Each time course shows the proportion of saccades to the correct disc, the target, as a function of saccade latency. For example, before going shopping, people can visualize how they will get to and from the grocery store, and imagine what they will buy when they get there. Nature Reviews Neuroscience, 2 3 , — Brunia, C. |
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