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Validation of a Bayesian Adaptive Estimation Technique in the Stop-Signal Task

View Article: PubMed Central - PubMed

ABSTRACT

The Stop Signal Task (SST), a commonly used measure of response inhibition, uses standard psychophysical methods to gain an estimate of the time needed to withhold a prepotent response. Under some circumstances, conventional forms of the SST are impractical to use because of the large number of trials necessary to gain a reliable estimate of the speed of inhibition. Here we applied to the SST an adaptive method for estimating psychometric parameters that can find reliable threshold estimates over a relatively small number of trials. The Ψ adaptive staircase, which uses a Bayesian algorithm to find the most likely parameters of a psychophysical function, was used to estimate the critical stop signal delay at which the probability of successful response inhibition equals 0.5. Using computational modeling and adult participants, estimates of stop signal reaction time (SSRT) based on the Ψ staircase were compared to estimates using the method of constant stimuli and a standard staircase method of adjustment. Results demonstrate that a reliable estimate of SSRT can be gained very quickly (20–30 stop trials), making the method very useful for testing populations that cannot maintain concentration for long periods or for rapidly obtaining multiple SSRT estimates from healthy adult participants.

No MeSH data available.


Estimates of critical SSD and response speed in Experiment 1.A) SSD estimate and values tested for a typical participant. B) Correlation between the (terminal) control CS estimate of SSD and each of the other three measures as a function of the number of stop trials completed. (C) Mean RT on correct go trials and % errors across the 20 blocks of trials. (D) Correlations between the CS estimate of SSRT and each of the other methods across trials.
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pone.0165525.g003: Estimates of critical SSD and response speed in Experiment 1.A) SSD estimate and values tested for a typical participant. B) Correlation between the (terminal) control CS estimate of SSD and each of the other three measures as a function of the number of stop trials completed. (C) Mean RT on correct go trials and % errors across the 20 blocks of trials. (D) Correlations between the CS estimate of SSRT and each of the other methods across trials.

Mentions: Panel A of Fig 3 shows a typical example of the Ψ method estimate and the SSD test values chosen to maximise the information gained from each trial. As Kontsevich and Tyler [18] explained in detail in their original paper, the method tends to choose values above and below the current most likely estimate rather than values close to the estimate itself. However, as can be seen in Fig 3A, those values do not necessarily follow an obviously systematic or regular progression in the way that a stepwise-adjusted staircase would. Panel B of Fig 3 depicts the correlation between the SSD calculated using the method of constant stimuli and each of the staircases after 1–40 trials. All three staircases had correlations with the CS estimate greater than 0.80 after 20 trials and greater than 0.86 after 30 trials. Fig 3B also includes correlations between the CS estimate and the mean of the two Ψ estimates.


Validation of a Bayesian Adaptive Estimation Technique in the Stop-Signal Task
Estimates of critical SSD and response speed in Experiment 1.A) SSD estimate and values tested for a typical participant. B) Correlation between the (terminal) control CS estimate of SSD and each of the other three measures as a function of the number of stop trials completed. (C) Mean RT on correct go trials and % errors across the 20 blocks of trials. (D) Correlations between the CS estimate of SSRT and each of the other methods across trials.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5120782&req=5

pone.0165525.g003: Estimates of critical SSD and response speed in Experiment 1.A) SSD estimate and values tested for a typical participant. B) Correlation between the (terminal) control CS estimate of SSD and each of the other three measures as a function of the number of stop trials completed. (C) Mean RT on correct go trials and % errors across the 20 blocks of trials. (D) Correlations between the CS estimate of SSRT and each of the other methods across trials.
Mentions: Panel A of Fig 3 shows a typical example of the Ψ method estimate and the SSD test values chosen to maximise the information gained from each trial. As Kontsevich and Tyler [18] explained in detail in their original paper, the method tends to choose values above and below the current most likely estimate rather than values close to the estimate itself. However, as can be seen in Fig 3A, those values do not necessarily follow an obviously systematic or regular progression in the way that a stepwise-adjusted staircase would. Panel B of Fig 3 depicts the correlation between the SSD calculated using the method of constant stimuli and each of the staircases after 1–40 trials. All three staircases had correlations with the CS estimate greater than 0.80 after 20 trials and greater than 0.86 after 30 trials. Fig 3B also includes correlations between the CS estimate and the mean of the two Ψ estimates.

View Article: PubMed Central - PubMed

ABSTRACT

The Stop Signal Task (SST), a commonly used measure of response inhibition, uses standard psychophysical methods to gain an estimate of the time needed to withhold a prepotent response. Under some circumstances, conventional forms of the SST are impractical to use because of the large number of trials necessary to gain a reliable estimate of the speed of inhibition. Here we applied to the SST an adaptive method for estimating psychometric parameters that can find reliable threshold estimates over a relatively small number of trials. The Ψ adaptive staircase, which uses a Bayesian algorithm to find the most likely parameters of a psychophysical function, was used to estimate the critical stop signal delay at which the probability of successful response inhibition equals 0.5. Using computational modeling and adult participants, estimates of stop signal reaction time (SSRT) based on the Ψ staircase were compared to estimates using the method of constant stimuli and a standard staircase method of adjustment. Results demonstrate that a reliable estimate of SSRT can be gained very quickly (20–30 stop trials), making the method very useful for testing populations that cannot maintain concentration for long periods or for rapidly obtaining multiple SSRT estimates from healthy adult participants.

No MeSH data available.