CNP Stop Signal

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Basic Task Description

The Stop-signal task is a widely used measure of response inhibition and is based on a horse-race model of stopping, which assumes that independent go and stop processes race against one another to determine whether a response is executed or inhibited (Logan and Cowan, 1984; Logan, 1994) (though the independence assumption can be relaxed (Boucher et al., 2007). The primary dependent variable of the task, Stop-signal reaction time (SSRT), provides an individualized measure of inhibitory control. In this task, participants are presented with a series of Go stimuli to which they are instructed to respond quickly. This speeded reaction time task establishes a prepotency to respond. On a subset of trials, the Go stimulus is followed, after a variable delay, by a stop-signal, to which participants are instructed to inhibit their response. The onset of a the stop signal is varied and depends on the participant’s performance, such that it is decreased after a previous failure to inhibit and increased after a previous inhibition. This one-up/one-down tracking procedure ensures that participants inhibit on approximately half of all stop trials, and the horse-race model allows for the estimation of stop-signal reaction time (SSRT), an individualized measure of a participant’s inhibitory ability that controls for difficulty level.

Task Procedure

In this version of the Stop-signal task, participants were shown a series of go stimuli (“X” or “O”) in the center of the screen and were told to press the left arrow button on the keyboard when they saw an “X” and to press the right arrow button on the keyboard when they saw an “O” (Go trials). On a subset of trials, a stop-signal (a 500 Hz tone presented through headphones) was presented a short delay after the go stimulus appeared and lasted for 250 ms (Stop trials). Participants were instructed to respond as quickly and accurately as possible on all trials, but to withhold their response on Stop trials (on trials with the tone). Participants were instructed that stopping and going were equally important.

On Stop trials, the delay of the onset of the stop-signal, or stop-signal delay (SSD), was varied, such that it was increased after the participant successfully inhibited in response to a stop-signal (making the next stop trial more difficult), and decreased after the participant failed to inhibit in response to a stop-signal (making the next stop trial less difficult). Each SSD increase or decrease was in 50 ms intervals. The SSD values were drawn from two interleaved staircases (or ladders) per block, resulting in 10 trials from each staircase for a total of 20 Stop trials per block, with values starting at 200 and 300 ms for ladders 1 and 2, respectively. At the end of the first block, the last SSD time from each staircase was then carried over to be the initial SSD for the second block. This one-up/one-down tracking procedure ensured that subjects successfully inhibited on approximately 50% of inhibition trials. Also as a result, difficulty level is individualized across subjects and both behavioral performance and numbers of successful stop trials are equated across subjects.

All participants received training on the Stop-signal task in the form of two practice blocks. The first practice block contained 32 random Go trials only to establish prepotency to respond. A second practice block contained 22 Go trials and 10 stop trials presented randomly. Subjects were required to score over 50% accuracy on Go trials per practice block in order to continue. Each experiment block contained 128 trials per block, 96 of which were Go trials and 32 of which were Stop trials (16 from Ladder 1, and 16 from Ladder 2), each presented randomly. Participants completed 2 successive blocks for a total of 256 trials.

All trials started with a 500 ms fixation cross in the center of the screen and included a 1000 ms fixed response interval. Subjects were allowed to respond at the start of stimulus presentation until the end of the 1000 ms fixed response interval. Each trial was separated by a fixed 100 ms delay.

On each trial, the screen was black with white characters. The letters were written in 26 size with Courier New font type.

Task Structure Detail

This is what we had worked on before, but could use updating. We'd like to capture a schema that can handle each of the tasks in the CNP, so please think general when editing -fws

  • Task Structure (please given an overview of the task procedures here [i.e., overall design, block, trial, and within-trial event structure and timing])
    • Participants performed two practice blocks and two experimental blocks (data from practice blocks not available)
      • Each experiment block contained 128 trials per block
        • 96 Go trials
        • 32 Stop trials
          • 16 Stop trials from Ladder 1
          • 16 Stop trials from Ladder 2
        • Participants completed 2 blocks for a total of 256 trials
    • Timing:
      • All trials started with a 500 ms fixation cross in the center of the screen
      • Each trial had a 1000 ms fixed response interval
        • The onset of Go stimuli was the beginning of each trial’s 1000 ms fixed response interval
        • The onset of Stop stimuli from two SSD ladders on a subset of trials (Stop trials) was variable
          • SSD started at 200 ms and 300 ms (for the two SSD ladders) for each participant
          • Mean SSD ranged from approximately 40-700 ms across first 1000 participants (from 256 total trials)
      • Each trial was separated by a fixed 100 ms delay
    • Go trials consisted of an “X” or “O” stimulus in the center of the screen for the 1000 ms fixed response interval
    • Stop trials consisted of an “X” or “O” stimulus in the center of the screen for the 1000 ms fixed response interval and a stop-signal (500Hz tone), which was presented a variable delay after the onset of the go stimulus and lasted for 250 ms
  • Stimulus Characteristics
    • sensory modality (e.g., visual, auditory, somatosensory, gustatory, olfactory): visual and auditory
    • functional modality (e.g., linguistic, spatial, numerical, categorical): ?
    • presentation modality (e.g., human examiner, paper, computer display, headphones, speaker): computer display, headphones
  • Response Characteristics
    • responses required - Left arrow key press, Right arrow key press, or inhibition of key press
      • effector modality (e.g., vocal, manual, pedal): Manual response and response inhibition
      • functional modality (e.g., words, drawing, writing, keypress, movement): keypress and response inhibition
    • response options (e.g., yes/no, go/no-go, forced choice, multiple choice [specify n of options], free response)- Go/No-Go
    • response collection (e.g., examiner notes, keyboard, keypad, mouse, voice key, button press)- Keyboard button press
  • Assessment/Control Characteristics ?
    • timing- see above
    • control/baseline-
    • other?

Task Schematic

Schemtic of the Stop-signal task.

SST Figure 2.png


Schematic of the horse-race model of stopping and estimation of Stop-signal reaction time (SSRT).

SSRT.png

Task Parameters Table

TaskParamTable.png

Stimuli

Go stimuli consisted of the letters “X” or “O”, which were presented in 26 size white Courier New font type in the center of a black screen.

A stop stimulus was a 500 Hz tone presented through headphones, which was presented a short delay after the go stimulus appeared and lasted for 250 ms.

A fixation stimulus was a fixation cross, which was presented in 26 size white Courier New font type in the center of a black screen.

Dependent Variables

The primary dependent variable is the Stop-signal reaction time (SSRT), which provides an individualized measure of inhibitory control. The use of a one-up/one-down tracking procedure ensures that participants inhibit on approximately half of all stop trials, which does not require an assumption of 50% inhibition, and allows us to use the quantile method to estimate SSRT (following Band et al., 2003).

Other dependent variables that may be of interest include the mean RT on Go trials and the standard deviation of RT on Go trials.

In contrast to other measures of response inhibition, such as the Go/No-Go task, percent inhibition is not a primary DV for the Stop-signal task, as this task is designed to ensure that participants inhibit on approximately 50% of all Stop trials.

Additional summary measures can be used to screen outliers (see below).

Table of all available variables.

SST Variables Table.png

Cleaning Rules

If any of the derived variables (those listed in variables Table above) are missing, for either one or both testing blocks, that participants should be flagged for exclusion.

There are several decisions to make when estimating SSRT from more than one block of Stop-signal task performance data, including whether to average across all available sessions or to use the last session run (based on the assumption that participants are closest to their 50% inhibition point at the end of a session); whether to use all trials of each session or the last half only (again based on the assumption that participants stabilize inhibitory performance near the end of a session); and whether to use data from all participants that completed the task regardless of performance, or to use either conservative or lenient criteria to exclude outliers, so as to avoid violating assumptions underlying the race-model of stopping. These questions have been systematically assessed in an independent dataset, which was randomly split into halves in order to evaluate reliability and repeatability of SSRT estimates derived following multiple approaches to data cleaning (Congdon et al., in preparation). Measures of reliability, including intraclass correlation coeffcients (ICC) and within-subject variability, and the resulting sample size, were used as indicators to evaluate the different strategies to data cleaning.

Our results suggest that an approach that uses the average of all available Stop-signal blocks, all trials of each block, and excludes outliers based on predetermined lenient criteria (defined below) yields reliable SSRT estimates and low within-subject variability, while not excluding too many participants from the total dataset. Specifically, this approach resulted in an ICC value of 0.79 and a within-subject variability estimate of 25.42 ms, while only excluding 7 (out of 184) participants. Critically, this approach also retains a broad distribution of SSRT values.

Based on these analyses, the following cleaning rules are suggested:

  • Use all trials from each testing block
  • Average across all available testing blocks for final summary scores
  • Exclude outliers that meet the following lenient criteria:
    • Percent inhibition on Stop trials less than 25% or greater than 75%
    • Percent correct responding on Go trials less than 60%
    • Percent incorrect Go trials greater than 10%
    • SSRT estimate that is negative or less than 50 ms

Code/Algorithms

Scoring of behavioral data proceeded as follows.

The mean, median and standard deviation of reaction time on Go trials were calculated only for Go trials in which participants correctly responded. Stop successful trials included only Stop trials on which participants successfully inhibited a response, and Stop unsuccessful trials included only Stop trials on which participants responded. Average stop-signal delay (SSD) was calculated from SSD values across both ladders. SSRT was estimated using the quantile method, which does not require an assumption of 50% inhibition (Band et al., 2003). Briefly, to calculate SSRT following the quantile method, all correct RTs from Go trials were arranged in ascending order. The proportion of failed inhibition, which is the proportion of Stop trials in which the participants responded, was calculated across both ladders. The quantileRT was determined by finding the RT corresponding to the proportion of failed inhibition. The average stop-signal delay (across both ladders) was then subtracted from the quantileRT in order to calculate SSRT (Band et al., 2003).

Scores are calculated from each block separately and then averaged to provide summary scores for the overall session. It is recommended that one use the final measures based on the overall session, as they provide more stable estimates of SSRT (Band et al., 2003; Congdon et al., in preparation).


Make 2 Filters: Procedure[Block] and Running[Block] and Procedure[Trial]

First filter to only include trials where Procedure[Block] = “StopProc” (these are the real trials)

Analyze the remaining trials in 2 different sets: Those with Running[Block] = “Block1” and those with Running[Block] = “Block2”

For each of those two sets (Block1 and Block2) do the following steps:

{

  • Block1_Direction_Errors = Number of trials where (Procedure[Trial] = "StGTrial" AND Go.RT> 0 AND Go.ACC= 0) OR (Procedure[Trial] = "StGTrial" AND Go.RT = 0 AND Blank.Resp != CorrectAnswer)
  • Block1_Percent_Go_Response = [Number of trials where Procedure[Trial] = "StGTrial" AND (Go.ACC=1 OR Blank.ACC=1)] / Number of trials where Procedure[Trial] = "StGTrial"

For the next calculation, you need the following values:

  • Go.RT for all trials where (Procedure[Trial] = "StGTrial") AND (GoAcc = 1 AND Go.RT > 0)
  • Blank.RT + 1000 for all trials where (Procedure[Trial] = "StGTrial") AND (GoACC = 0 AND Go.RT = 0 AND Blank.ACC = 1)
  • Block1_Mean_RT = Mean of all values that you just got from Go.RT and Blank.RT + 1000
  • Block1_Median_RT = Median of all values that you just got from Go.RT and Blank.RT + 1000
  • Block1_StDev_RT = Standard deviation of all values that you just got from Go.RT and Blank.RT + 1000

For the next step, you need to get have these numbers:

  • GoDur - 50 for all trials where Procedure[Trial] = "StITrial" AND (Go1s.RT=0 and Inhs.RT=0 and Go2S.RT=0 and Blanks.RT=0)
  • GoDur + 50 for all trials where Procedure[Trial] = "StITrial" AND (Go1s.RT!=0 or Inhs.RT!=0 or Go2S.RT!=0 or Blanks.RT!=0)
  • GoDur2 - 50 for all trials where Procedure[Trial] = "StITrial2" AND (Go1s2.RT=0 and Inhs2.RT=0 and Go2s2.RT=0 and Blanks.RT=0)
  • GoDur2 + 50 for all trials where Procedure[Trial] = "StITrial2" AND (Go1s2.RT!=0 or Inhs2.RT!=0 or Go2s2.RT!=0 or Blanks.RT!=0)
  • Block1_Ladder1Mean = Mean of all GoDur values from the previous statements (there should be 16 total)
  • Block1_Ladder2Mean = Mean of all GoDur2 values from the previous statements (there should be 16 total)
  • Block1_SSD50 = Mean of ALL the values you just got from the previous statements (there should be 32 total values that you're taking the mean of)


  • Block1_PctInhib_Ladder1 = [Number of Trials where Procedure[Trial] = "StITrial" AND (Go1s.RT=0 and Inhs.RT=0 and Go2s.RT=0 and Blanks.RT=0)] / (Number of Trials where Procedure[Trial] = "StITrial")
  • Block1_PctInhib_Ladder2 = [Number of Trials where Procedure[Trial] = "StITrial2" AND (Go1s2.RT=0 and Inhs2.RT=0 and Go2s2.RT=0 and Blanks.RT=0)] / (Number of Trials where Procedure[Trial] = "StITrial2)
  • Block1_Percent_Inhib = Mean of Block1_PctInhib_Ladder1 and Block1_PctInhib_Ladder2
  • Block1_Quantile_Value = 1 - Block1_Percent_Inhib
  • Block1_SSRT = Block1_Median_RT - Block1_SSD50
  • CorrectGoRT on StopProc trials: As a reminder, these are Go.RT for all trials where (Procedure[Trial] = "StGTrial") AND (GoAcc = 1 AND Go.RT > 0), and Blank.RT + 1000 for all trials where (Procedure[Trial] = "StGTrial") AND (GoACC = 1 AND Go.RT = 0). Use these CorrectGoRT on StopProc trials to calculate Block1_Quant_RT:
  • Block1_Quant_RT = Quantile calculation of all the values from Go.RT and Blank.RT + 1000 (the values used in the calculations where you calculated Block1_Mean_RT, etc), taken using Quantile_Value. So if Quantile_Value = .85, you'd take the .85-quantile of the distribution of values from Go.RT and (Blank.RT + 1000)
  • Block1_SSRT_Quant = Block1_Quant_RT - Block1_SSD50

}

Then, do all those same calculations that were in the braces, but using the data points that were from Running[Block] = “Block2” (and save the variables as Block2_whatever, rather than Block1)

Then average the above values together to get a measure of overall task performance:

All the values that we need outputted are in bold. See also Variable List.


The following text instructions can be adapted for any program. These instructions are currently implemented in Stone’s scoring scripts for the scoring of LA2K variables. These steps have been implemented in separate Matlab scripts by Eliza and have been used to verify Stone’s scoring scripts (October 2009). These steps have also been used to manually score the data and verify Stone’s scoring scripts (October 2009).

One thing that may vary slightly across different programs is the calculation of a quantile.

Data Distributions

References

Band, G. P., van der Molen, M. W., and Logan, G. D. (2003). Horse-race model simulations of the stop-signal procedure. Acta Psychol, 112, 105-142.

Boucher, L., Palmeri, T. J., Logan, G. D., and Schall, J. D. (2007). Inhibitory control in mind and brain: An interactive race model of countermanding saccades. Psychol Rev, 114 (2), 376-97.

Logan, G. D., and Cowan, W. B. (1984). On the ability to inhibit through and action: a theory of an act of control. Psychol Rev, 91, 295-327.

Logan, G. D. (1994). On the ability to inhibit thought and action: A users’ guide to the stop signal paradigm. In: Dagenbach, D., Carr, T. H. (Eds.), Inhibitory Processes in Attention, Memory and Language. Academic Press, San Diego, pp. 189-239.