experiment 2 - example subject (16) - Go-RTs

What happens when the brain awaits a signal of uncertain arrival time, as when a sprinter waits for the starting pistol? And what happens just after the starting pistol fires? Using functional magnetic resonance imaging (fMRI), we have discovered a novel correlate of temporal expectations in several brain regions, most prominently in the supplementary motor area (SMA). Contrary to expectations, we found little fMRI activity during the waiting period; however, a large signal appears after the “go” signal, the amplitude of which reflects learned expectations about the distribution of possible waiting times. Specifically, the amplitude of the fMRI signal appears to encode a cumulative conditional probability, also known as the cumulative hazard function. The fMRI signal loses its dependence on waiting time in a “countdown” condition in which the arrival time of the go cue is known in advance, suggesting that the signal encodes temporal probabilities rather than simply elapsed time. The dependence of the signal on temporal expectation is present in “no-go” conditions, demonstrating that the effect is not a consequence of motor output. Finally, the encoding is not dependent on modality, operating in the same manner with auditory or visual signals. This finding extends our understanding of the relationship between temporal expectancy and measurable neural signals.

The Stop Signal Task is a laboratory task assessing inhibitory control. In the task, participants were seated in a comfortable desk chair behind a personal computer of which the screen was placed at approximately 60 cm distance. The experimenter was seated next to the participant and held track of performance through a hand computer. The participant held a button box in each hand and was instructed to press buttons with the thumb. The task consisted of 64 practice trials and 256 experimental trials, of which 64 stop-trials and 192 go-trials. On both go and stop-trials, participants saw a fixation point presented for 500 ms. The fixation point was followed by a go-stimulus (a picture of an airplane) that was displayed for 1,000 ms and that was presented in the center of the screen. In response to the go-stimulus, participants were required to press a response button that corresponded to the direction the plane was flying in (left or right). On stop-trials, a white cross was superimposed on the go-stimulus and acted as stop-signal. Participants were instructed not to press any button when a trial contained a stop-signal. Trials were presented in a semi-random fixed order. The longer the delay between go-signal (plane) and stop-signal (white cross; the stop-signal-delay, SSD), the more difficult it was to inhibit the response. To ensure that the percentage of inhibited responses approached 50% for each individual, SSD was systematically varied. If participants inhibited correctly, SSD lengthened by 50 ms; if participants failed to inhibit their response, SSD shortened by 50 ms (see Luman et al., 2004). In order to avoid non-responses or highly delayed responses, the experimenter provided instructions to react as fast as possible after an omission error and after four highly delayed responses on go-trials. Mean reaction time (xcorrect = MRT) over all correct go-trials, Stop Signal Reaction Time (SSRT) and the number of commission and omission errors were assessed.

This dataset reports on the operation state of traffic signals in Austin, TX. Traffic signals enter flash mode when something is preventing the signal from operating normally. This is typically the result of a power surge, power outage, or damage to signal equipment. A signal may also be intentionally placed into flash mode for maintenance purposes or be scheduled to flash overnight.

You can view an interactive map of flashing traffic signals here:

https://data.mobility.austin.gov/signal-monitor

Approximately 90% of the City’s signals communicate with our Advanced Transportation Management System. When these signals go on flash, they will be reported in this dataset. Although we are extending communications to all signals, approximately 10% are not currently captured in this dataset. It also occasionally happens that the event that disables a traffic signal also disables network communication to the signal, in which case the signal outage will not be reported here.

In this dataset the distinction between scheduled and unscheduled flash is identified by the 'operation state' column. A signal that is in unscheduled flash mode will have a status of 2 or 7. A signal that is in in scheduled flash mode will have a status of 1.

This product is for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. It does not represent an on-the-ground survey and represents only the approximate relative location of traffic signals.

About this dataset

Context

The dataset tabulates the population of Signal Mountain by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Signal Mountain. The dataset can be utilized to understand the population distribution of Signal Mountain by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Signal Mountain. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Signal Mountain.

Key observations

Largest age group (population): Male # 5-9 years (615) | Female # 40-44 years (573). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Signal Mountain population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Signal Mountain is shown in the following column.
• Population (Female): The female population in the Signal Mountain is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Signal Mountain for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Signal Mountain Population by Gender. You can refer the same here

A central experimental task in executive control research is the Stop-signal task, which allows measuring the ability to inhibit dominant responses. A crucial aspect of this task consists of varying the delay between the Go- and Stop-signal. Since the time necessary to administer the task can be long, a method of optimal delay choice was recently proposed: the PSI method. In a behavioral experiment, we show a variant of this method, the PSI marginal method, to be unable to deal with the Go-response slowing often observed in the Stop-signal task. We propose the PSI adjusted method, which is able to deal with this response slowing by correcting the estimation process for the current reaction time. In several sets of behavioral simulations, as well as another behavioral experiment, we document and compare the statistical properties of the PSI marginal method, our PSI adjusted method, and the traditional staircase method, both when reaction times are constant and when they are linearly increasing. The results show the PSI adjusted method’s performance to be comparable to the PSI marginal method in the case of constant Go-response times, and to outperform the PSI marginal method as well as the staircase methods when there is response slowing. The PSI adjusted method thus offers the possibility of efficient estimation of Stop-signal reaction times in the face of response slowing.

Go-RTs and error rates means and standard deviations on go trials as a function of picture valence values.

This open document spreadsheet (.ods) file contains summary data from all subjects in all conditions from "Subthreshold transcranial magnetic stimulation applied after the go-signal facilitates reaction time under control but not startle conditions" Published in the European Journal of Neuroscience.

About this dataset

Context

The dataset tabulates the population of Signal Hill by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Signal Hill. The dataset can be utilized to understand the population distribution of Signal Hill by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Signal Hill. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Signal Hill.

Key observations

Largest age group (population): Male # 30-34 years (619) | Female # 30-34 years (692). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Signal Hill population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Signal Hill is shown in the following column.
• Population (Female): The female population in the Signal Hill is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Signal Hill for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Signal Hill Population by Gender. You can refer the same here

This dataset contains behavioral data from an experiment with rats. This is a summary of the experiment: We developed a computerized protocol to train rats, in high-throughput facilities, to perform an auditory delayed comparison task. All training takes place in three-port operant conditioning chambers, in which ports are arranged side-by-side along one wall, with two speakers placed above the left and right nose ports. A visible light-emitting diode in the centre port signals the availability of each trial. Rat subjects initiate a trial by inserting their nose into the center port, which causes the center light to turn off. Rats must keep their nose in the centre port (fixation period) until an auditory go cue (a 6-kHz pure tone for 200 ms) signals the end of fixation. Only after the go cue can subjects withdraw and orient to one of the side ports to receive a reward of water. During the fixation period two auditory stimuli, sa and sb, separated by a variable delay, are played for 400 ms, with short delay periods of 250 ms inserted before sa and after sb. Rats should judge which out of the two stimuli, sa and sb, had the greater SPL standard deviation. If sa > sb, the correct action is to poke the nose into the right-hand nose port to collect the reward, and if sa < sb, rats should orient to the left-hand nose port. Trial durations are independently varied on a trial-by-trial basis, by varying the delay interval between the two stimuli, which can be as short as 2 s or as long as 12 s. The delay between the two stimuli is varied on a trial-by-trial basis, which makes the task more challenging and requires the rats to maintain their attention and memory over longer periods. Rats progressed through a series of shaping stages before the final version of the delayed comparison task, in which they learned to: associate light in the center poke with the availability of trials; associate special sounds from the side pokes with reward; maintain their nose in the center poke until they hear an auditory go signal; and compare the two sa and sb stimuli.
stages descriptions:

There are six stages involved in the training process, each of which builds on the previous stage and introduces new aspects of the task. In stage 1, rats learn to associate the lights on either side with the availability of trials and poke into the lightened side poke to get a reward.

In stage 2, rats learn to initiate a trial by poking their nose into the center port and waiting for a go cue sound to signal the end of the fixation period. They can then withdraw and orient to one of the side ports to receive a reward.

In stage 3, the fixation period is gradually increased in duration over successive trials. This is designed to increase the rats ability to maintain their attention and focus during the task.

in the dataset, only the three first stages’ data is included.

. then a go cue sound plays. that means that rat can

This dataset contains macaque single unit recordings from the orbitofrontal cortices (subjects J and T), for stop signal task and economic choice task.The data is stored in .mat format, and has separate files for neural activity aligned to go signal and stop signal

In this paper, three different methods for brain signal acquisition are presented. All methods deal with feature extraction method of Electroencephalogram (EEG) based P300 waves. The performance of the three methods is investigated through backpropagation neural network classifier. EEG-P300 recordings provide an important means of brain-computer communication, but their classification accuracy and transfer rate are limited by unexpected signal variations due to artifacts and noises. A comparison of extraction methods (i.e., AAR, JADE, and SOBI) entailing time-series EEG signals is proposed. Finally, the promising results reported here reflect the considerable potential of EEG for the continuous classification of mental states. Advanced Science Letters, Volume 20, Number 1, January 2014 , pp. 80-85(6)

In recording the EEG signals are often contamination signal called artifacts. There are different kinds of artifacts such as power line noise, electromyogram (EMG), electrocardiogram (ECG) and electrooculogram (EOG). This research will compare two methods for removing artifacts, i.e. ICA and PCA methods. EEG signals are recorded on three conditions, which is normal conditions, closed eyes, and blinked eyes. After processing, the dominant frequency of the EEG signal is obtained in the range of 12-14 Hz (alpha-beta) either on normal conditions, closed eyes, and blinked eyes. From processing with ICA and PCA methods found that ICA method are better than PCA in terms of the separation of the EEG signals from mixed signals. International Conferences on Technology, Informatics, Management, Engineering & Environtment, 19-21 August 2014, Bandung Indonesia.

Inter-animal communication allows signals released by an animal to be perceived by others. Scent-marking is the primary mode of such communication in giant pandas (Ailuropoda melanoleuca). Signal detection theory propounds that animals choose the substrate and location of their scent marks so that the signals released are transmitted more widely and last longer. We believe that pandas trade off scent-marking because they are an energetically marginal species and it is costly to generate and mark chemical signals. Existing studies only indicate where pandas mark more frequently, but their selection preferences remain unknown. This study investigates whether the marking behavior of pandas is consistent with signal detection theory. Feces count, reflecting habitat use intensity, was combined with mark count to determine the selection preference for marking. The results showed that pandas preferred to mark ridges with animal trails and that most marked tree species were locally dominant. In...

The Stop-Signal Task - Arrows aims to measure behavioral inhibition by having the child inhibit their reaction in response to a quickly appearing signal. During the task, left- and right-pointing arrows appear on the screen. When the arrow points left, the child has to press the left button, and when the arrow points right, the child has to press the right button. When the arrrow is colored red, the child has to withhold pressing any buttons. The child has to respond to the right signal as quickly and as accurately as possible. Both accuracy and reaction time are calculated in the Go and No Go conditions.

Description of columns in event files (see the associated publication for more details): - onset: Onset of all events on the number line marking experiment, in seconds. Each trial was divided into two events, an “encoding” phase and a “marking” phase. Each trial is represented in 2 rows, one for each phase. As discussed in the paper, we found a 1 TR (2 seconds) displacement between the logged behavioral data and the fMRI data. To account for this, the onsets reported here have already been corrected by subtracting 2 seconds from the value logged on the behavioral protocol. - duration: Duration of the encoding phase corresponds to the time that the probe was presented (0.5s), plus the minimum time of the inter-trial interval (“hold” period) which corresponded to 2.5s, for a total of 3 seconds. We reasoned that this was our best estimate for the encoding phase since participants will begin monitoring the change of the hold signal at some time after the minimum hold time and begin preparing an action. Duration of the hold period can be inferred by calculating the difference between the (encoding + 0.5) and the marking phase onsets. During the marking phase, duration corresponds to the 3-second deadline to mark the line. - trial_type: “Pos” = 0 - 100 line; “Neg” = -100 - 100 line; “Word” = Word control task. “break” corresponds to the 6 seconds transition between trial types. - phase: encoding, marking. “break” corresponds to the 6-second transition between trial types. - probe: number or word presented to a participant on a given trial. - line_pct: proportion of line extent corresponding to the probe target location on each trial. - response: response coded as proportion of line extent - RT: reaction time measured from the moment the cursor is activated after the “go” signal. - RTHold: reaction time from the moment the “go” signal appears until the participant clicks to activate the cursor. - catch: 1 = catch trial; 0 = real trials - missed: 1 = participant failed to mark the line on time; 0 = participant marked the line in time. - problem_trial: Due to a computer error the screen froze during 15 trials divided among seven participants. In our analyses, we generated volume-scrubbing regressors that excluded these error trials.

Skin conductance data recorded in 67 healthy subjects while they performed a go/no-go task. Most subjects performed 4 sessions of the task. Some have less than 4 due to loss of signal in one of the sessions. For details of the task, see paper: http://www.jneurosci.org/content/early/2019/06/12/JNEUROSCI.0508-19.2019.abstract Briefly, the task contains approximately 2/3 go and 1/3 no-go trials. Correct go and no-go responses were rewarded and incorrect responses were punished with either a dollar or nickel. Thus, there are 8 conditions: 'GS_dollar','GS_nickel','GE_dollar','GE_nickel','NGS_dollar','NGS_nickel','NGE_dollar','NGE_nickel' where GS: go success, GE: go error, NGS: no-go successs, NGE: no-go errorThus, for each subject, data for each condition and each session has been named accordingly. The data are organized by subject. Each subject has a folder which contain data for each session and each task condition. Within the task condition file, time series of skin conductance signal can be found for each trial.Onsets (zipped file): onsets of each task condition. The data are organized by session. For each session, a 1x8 cell contains onsets for 8 conditions with the order as follows: 'GS_dollar','GS_nickel','GE_dollar','GE_nickel','NGS_dollar','NGS_nickel','NGE_dollar','NGE_nickel'The data for the onsets are in MATAB format.Skin conductance and onset data are in MATLAB format.Behavioral: behavioral performance (accuracy rate and response time) and basic demographics.All data have been de-identified. Information about age and gender for each subject may be found in the demographic text file.Further info can be found in readme file.Any questions may be directed to thang.le@yale.edu.

Gene family function annotations predicted by MPP-H, by MPP-O, by MPP-I, by MPP-16S, by PP (matched with MPP-H), by PP (matched with MPP-O) and by PP (matched with MPP-I and MPP-16S), all provided in separate table files. In each table, rows are gene families (first column lists the COG or NOG ID), columns are GO functions (the header row lists GO ID) and values are the precision (Pr) thresholds at which a GO term was assigned to a COG or a NOG. Values of Pr < 0.1 are listed as Pr = 0. Table contains the set of GO terms with at least one prediction available at the threshold Pr ≥ 0.1. (7Z 18097 kb)

Error rates means and standard deviations on no-go trials as a function of picture valence values.

Additional file 1: Table S1. Summary of all sample data. Table S2. The candidate genes of top 1% Fst. Table S3. The candidate genes of top 1% π-Ratio. Table S4. The candidate genes of top 1% XP-EHH. Table S5. Summary information of candidate genes shared by three methods and other focused candidate genes. Table S6. GO enrichment analysis on the candidate gene identified by top 1% Fst. Table S7. GO enrichment analysis on the candidate gene identified by top 1% π-Ratio. Table S8. GO enrichment analysis on the candidate gene identified by top 1% XP-EHH. Table S9. KEGG enrichment analysis on the candidate gene identified by top 1% Fst. Table S10. KEGG enrichment analysis on the candidate gene identified by top 1% π-Ratio. Table S11. KEGG enrichment analysis on the candidate gene identified by top 1% XP-EHH. Table S12. Results of D- statistics in the form, G. sonnerattii), G. varius). Table S13. Results of D-statistics in the form, G. g. gallus), G. varius). Table S14. Significant introgression region between G. sonnerattii and Euramerican gamecocks. Table S15. Candidate introgression genes between G. sonnerattii and Euramerican gamecocks. Table S16. GO enrichment analysis on the candidate introgression genes between G. sonnerattii and Euramerican gamecocks. Table S17. KEGG enrichment analysis on the candidate introgression genes between G. sonnerattii and Euramerican gamecocks. Table S18. Significant introgression region between G. g. gallus and SEA. Table S19. Candidate introgression genes between G. g. gallus and SEA. Table S20. Go enrichment analysis on candidate introgression genes between G. g. gallus and SEA. Table S21. KEGG enrichment analysis on the candidate introgression genes between G. g. gallus and SEA. Table S22. Candidate introgression genes between G. g. gallus and Tur. Table S23. Candidate introgression genes between G. g. gallus and XSBN. Table S24. Candidate introgression genes between G. g. gallus and JPN.