Elsevier

Biological Psychology

Volume 77, Issue 3, March 2008, Pages 324-336
Biological Psychology

Effects of varying stop-signal probability on ERPs in the stop-signal task: Do they reflect variations in inhibitory processing or simply novelty effects?

https://doi.org/10.1016/j.biopsycho.2007.11.005Get rights and content

Abstract

The aim of the present study was to determine whether ERP modulations associated with varying the probability of the stop-signal in the stop-signal task reflect variations in inhibitory processing, or whether they simply reflect general arousal associated with novel stimuli. This was achieved by examining the effects of probability on a control “ignore-signal” stimulus in addition to the stop-signal. ERP findings revealed large fronto-central N1 and P3 components that were larger in amplitude for stop-signals than ignore-signals, and when stimuli were rare (30%) compared to frequent (70%). However, probability effects were not greater for stop-signals compared to ignore-signals, discounting an interpretation exclusively in line with inhibitory processing. A principal components analysis (PCA) revealed a slow-wave ERP component that partially accounted for these probability effects. Together, the present findings indicate that ERP differences between rare and frequent stop-signals did not primarily reflect varying inhibitory requirements, but rather may be confounded by novelty effects.

Introduction

Effective responding in a dynamic environment requires an elegant balance of response activation and inhibition processes. If factors favour the inhibition process, then a response is likely to be stopped in time for amendment. If, however, factors favour the execution of the response process, inhibition will be less capable of stopping a now inappropriate response. In a laboratory setting, the stop-signal task has been used to measure the efficiency and latency of these distinct but interrelated processes (Logan, 1994). During the stop-signal task, participants respond with button presses to a choice reaction time (RT) Go task and inhibit responses on a small portion of trials upon the presentation of a stop-signal (Logan and Burkell, 1986, Logan et al., 1984). The horse-race model is used to explain stop-signal task performance whereby the Go response (triggered by the Go stimulus) races against the inhibitory response (triggered by the stop-signal), with the outcome of the race dependent upon the relative speeds of the Go and inhibitory responses, as well as whether the inhibition process is triggered. Manipulating the delay between the Go and stop-signal stimuli can bias the trial-to-trial outcome of inhibition, as it is much easier to inhibit a response at shorter than at longer stop-signal delays. At a global task level, inhibition difficulty may also be manipulated by varying the probability of stop-signal trials. Presenting stop-signals less frequently encourages a bias towards response activation, as evidenced by faster Go RT, making it more difficult to inhibit responses (Lappin and Eriksen, 1966, Logan and Burkell, 1986, Logan et al., 1984, Ollman, 1973, Ramautar et al., 2004). The positive relationship between the probability of the stop-signal and inhibition accuracy is a well-established finding (Lappin and Eriksen, 1966, Logan and Burkell, 1986, Logan et al., 1984, Ollman, 1973, Ramautar et al., 2004).

An advantage of the stop-signal task over other inhibition tasks is that the horse-race model provides a method of estimating the speed of the inhibitory response, known as stop-signal reaction time (SSRT). SSRT has been shown to be relatively constant, at approximately 200 ms, in healthy adults across a number of different forms of movement, including typing, button-pressing, and eye movement (Logan, 1994). However, as the horse-race model works primarily on latency-based hypotheses, it is not concerned with the actual nature of the processes underlying successful inhibition and cannot provide accurate information about variations in the inhibition process itself. Event-related potentials (ERPs) offer a highly accurate method of examining the temporal, and to a more limited extent spatial, properties of the response and inhibition processes. Two ERP components that have been variably linked with inhibition are the N2, a negative component peaking approximately 200 ms after the onset of an inhibition-evoking stimulus with a frontal maximum (Eimer, 1993, Kopp et al., 1996, van Boxtel et al., 2001), and the P3, a positive component peaking approximately 300 ms post-stimulus with a fronto-central or central maximum (Dimoska et al., 2006, Kok et al., 2004, Ramautar et al., 2004, Ramautar et al., 2006). In the Go/nogo task, where frequent Go trials require a response and infrequent nogo trials require the inhibition of this prepotent response, nogo trials typically show enhanced N2 (Eimer, 1993, Falkenstein et al., 1995, Kopp et al., 1996) and P3 amplitudes (Pfefferbaum and Ford, 1988, Pfefferbaum et al., 1985) relative to Go trials, supporting a role in response inhibition. More recently, however, the functional significance of the N2 has been challenged, with some researchers suggesting a role in the detection of conflict between concurrently activated responses, rather than inhibition per se (Donkers and van Boxtel, 2004, Nieuwenhuis et al., 2003, Van Veen and Carter, 2002). In contrast, the inhibitory role of the P3 has been strengthened by recent stop-signal research with studies finding that the P3 has a source in or near the primary motor cortex, a region believed to be responsible for mediating stop-signal inhibition (Kok et al., 2004, Ramautar et al., 2004), and that P3 amplitude is larger in subjects requiring greater inhibitory activation to successfully stop fast responses, relative to subjects with slower responses (Dimoska et al., 2006).

A review of the inhibition literature reveals that decreasing stop-signal, or nogo, stimulus probabilities results in an increase in the amplitude of the N2 and P3 components, suggesting an increase in the activation of inhibition processes (Banquet et al., 1981, Bruin and Wijers, 2002, Donkers and van Boxtel, 2004, Eimer, 1993, Nieuwenhuis et al., 2003, Pfefferbaum and Ford, 1988, Ramautar et al., 2004). Ramautar et al. (2004) found that a frontal N2 and centro-parietal P3 were larger in amplitude for rare (20%) compared to frequent (50%) stop-signals. This was interpreted as reflecting greater inhibitory activation to counteract the larger bias towards responding when stop-signals were presented rarely. P3 also peaked later for rare stop-signals, suggesting increased difficulty of switching from a Go response bias to an inhibitory requirement. Together these findings suggest that the probability effects on N2 and P3 reflect modulations of inhibitory processes. However, N2 and P3 amplitudes have been shown to vary inversely with stimulus probability regardless of the response assigned to the stimulus (Banquet et al., 1981, Bruin and Wijers, 2002, Czigler et al., 1996, Donkers and van Boxtel, 2004). Rare stimuli have been found to evoke a greater cortical response than frequent stimuli because of the fact that they are novel within the prevailing stimulus context (Duncan-Johnson and Donchin, 1977). Nevertheless, in the Go/nogo task, the N2 rare > frequent effect has been found to be larger for nogo than Go stimuli, suggesting differences between probability conditions that are specific to the inhibitory nogo trials (Eimer, 1993, Nieuwenhuis et al., 2003). Therefore, ERP modulations with stop-signal probability may reflect the aggregate activity of novelty effects and inhibitory processing.

The present study examined the effects of varying stop-signal probability on performance and ERPs in the stop-signal task. However, as ERP probability effects may be due to the arousing properties of presenting stop-signals rarely rather than increased inhibitory activation, we attempted to dissociate these two factors. This was achieved by examining the effects of varying the probability of a task-irrelevant ignore-signal in the stop-signal task (Bedard et al., 2003, Bedard et al., 2002, Dimoska et al., 2006, Schmajuk et al., 2006), which subjects were instructed to ignore. As the ignore-signal was an irrelevant stimulus that did not require any action, any differences between rare and frequent probability conditions that occurred for stop-signal but not ignore-signal trials, as shown by a significant interaction between trial (stop-signal vs. ignore-signal) and probability (30% vs. 70%), could be attributed to inhibition. In contrast, a lack of interaction would suggest that probability differences may be better explained by novelty effects.

In order to determine whether inhibitory requirements have been affected by a manipulation of stimulus probability, it must be determined whether the bias towards or against response activation varied in line with these manipulations. That is, when responses are faster, they are more difficult to inhibit and require greater or faster inhibitory activation in order to successfully stop responding (Logan, 1994, Ramautar et al., 2004). In Ramautar et al. (2004), a bias towards responding was established through an examination of ERPs to Go stimuli whereby a larger Go-P3 for rare compared to frequent stop-signals indicated enhanced Go response processing. In the present study, the lateralised readiness potential (LRP) was used to determine response activation changes. The LRP is an electrophysiological index that reflects motor-related processes specifically related to the preparation of a left or right hand response, but which eliminates most other lateralised activity (Band and van Boxtel, 1999, Coles, 1989). Enhanced LRP amplitude reflects greater response activation once a specific left or right hand response is selected (Coles, 1989, Kutas and Donchin, 1980). It was hypothesised that rare stop-signals would encourage a faster, more impulsive response style, relative to frequent stop-signals, and that this would be reflected as greater LRP amplitude.

The effects of varying stop-signal probability were also examined on ERP components time-locked to the overt response. A negative–positive complex has been observed following a response on failed stop-signal trials, which may reflect the error-negativity (Ne) and error-positivity (Pe) (Dimoska et al., 2006, Kok et al., 2004, Ramautar et al., 2004, Ramautar et al., 2006). Ne amplitude has been interpreted as reflecting the detection of error (Falkenstein et al., 2000) or perhaps response-conflict (Van Veen and Carter, 2002). Amplitude is typically larger for faster errors (Gehring et al., 1995), but remains unaffected by error rate (Falkenstein et al., 2000). Pe amplitude has been interpreted as the subjective and affective assessment of errors; larger amplitude in subjects with a low compared to high frequency of errors is believed to reflect the increased emotional significance of an error when it occurs rarely (Falkenstein et al., 2000). In contrast, larger Pe amplitude has been found in the stop-signal task for rare compared to frequent stop-signal trials, despite a greater rate of errors in the rare condition, suggesting it was the probability of the stimulus and not the error that increased Pe amplitude (Ramautar et al., 2004). No probability effects have been observed for Ne.

In sum, it was anticipated that rare stop-signals would be associated with a bias towards responding, as reflected by a faster Go RT, reduced inhibition accuracy and larger LRP amplitude, relative to frequent stop-signals. Stop-signal ERPs, namely N2 and P3 would be larger in amplitude for rare compared to frequent stop-signals, and if these reflect increases in inhibitory activation, then the probability differences would be greater for stop-signals than ignore-signals. Finally, it was hypothesised that rare stop-signals would be associated with larger amplitude of the response-locked Pe, but not Ne.

Section snippets

Participants

Thirty adults (10 males) aged 17 years 11 months to 31 years 8 months (M = 22.1 years, S.D. = 3.3 years) participated in this study. Subjects were included if they had never suffered an epileptic seizure, serious head injury, period of unconsciousness or any psychiatric condition. Each subject reported no problems with hearing and had normal or corrected-to-normal vision. Two subjects were left-handed. Informed consent was obtained from all subjects after the testing equipment had been explained,

Performance measures

Table 1 shows the means and standard deviations for all performance measures. Measures were compared between the two experimental conditions: rare stop-signals vs. frequent stop-signals.

SSRT did not differ between probability conditions (F < 1). Overall inhibition accuracy was greater for frequent compared to rare stop-signals (F = 18.4, p < .001), while a linear effect across conditions revealed that inhibition accuracy decreased with an increase in stop-signal delay (F = 101.4, p < .001; see Fig. 1,

Discussion

Stop-signal probability was varied in the stop-signal task to determine whether rare stop-signals would be associated with greater inhibitory activation compared to frequent stop-signals. The effects of varying a task-irrelevant ignore-signal was also examined, allowing a dissociation of ERP differences due to inhibitory requirements and simple probability-related novelty effects. This design also allowed the number of trials containing an auditory tone to be equated between experimental

Acknowledgements

We would like to thank Dr. Janette Smith for editorial comments on an earlier version of this paper and Rodney Davies for advice on the technical aspects of the study.

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