Introduction
In task-switching paradigms participants shift between task rules (e.g., “Is a number greater or lesser than five?”, “Is a number odd or even?”) for the same set of stimuli (e.g., digits 1 through 9). The comparison of trials on which task rules switch with those on which task rules repeat is valuable for investigating those processes that are associated with behavioral control, presumed to be particularly important during task-rule switches. Indeed a difference in performance is typically observed between switch and repeat trials. This switch cost has been associated with at least two critical processes of behavioral control: task preparation and response selection.
Task preparation is hypothesized to involve at least partial retrieval of task rules (Allport & Wylie,
1999; de Jong,
2000; Gilbert & Shallice,
2002; Mayr & Kliegl,
2000; Meiran,
1996; Monsell,
1996; Rubinstein, Meyer & Evans,
2001; Sohn & Anderson,
2001; Yeung & Monsell,
2003), referred to as task set reconfiguration (Rogers & Monsell,
1995). This process can be triggered by a cue that indicates the identity (Sohn & Carlson,
2000) or probability (Dreisbach, Haider & Kluwe,
2002) of a subsequent task. Evidence for task preparation comes from the observation that the more information and preparatory time that participants are granted before a task, the better their performance. The benefit of preparation for performance is measured as a function of the cue–stimulus interval (CSI). An increase in its duration produces a reduction in switch cost (RISC; Meiran,
1996; Monsell & Mizon,
2006). More recent evidence has shown that priming of cue encoding also contributes to RISC (Logan & Schneider,
2006). In either case, advance cuing appears to prospectively benefit performance during task switching.
Task preparation however does not typically eliminate switch cost, leaving behind a “residual” switch cost. It has been suggested that this residual switch cost can be accounted for by processes that occur at the level of response selection. In particular these processes may contribute to inhibition of irrelevant but competing task sets, which would be expected to interfere with and thus prolong the selection of a response during a subsequent switch but not repetition trial, producing a switch cost. That is, response selection on one trial affects performance on the next. This conclusion is supported by diminished switch cost following trials in which response selection is absent. For instance, Schuch and Koch (
2003) measured switch cost following no-go trials, in which a stimulus appeared on screen but a signal instructed participants to withhold their response, and found that switch cost was eliminated on the trial immediately following. In subsequent studies they and others have demonstrated that processes that occur at the level of response-selection, independent of motor execution or inhibition, are the critical factor (Philipp, Jolicoeur, Falkenstein & Koch,
2007; Verbruggen, Liefooghe, Szmalec & Vandierendonck,
2005; Verbruggen, Liefooghe & Vandierendonck,
2006).
Independently measured, task preparation and response selection provide a clear account of behavior control; task rules that are made active in working memory either facilitate or interfere with selection of responses during target onset. However, when considered together, the supporting findings are paradoxical. Specifically, whereas the RISC effect observed on go trials suggests that task-set preparation must have been effective in activating or reconfiguring the task, the complete absence of switch cost following no-go trials suggests the opposite. If task preparation and response-selection processes independently contribute to switch cost then eliminating one should eliminate only that component of the switch cost, not both as observed by Schuch and Koch (
2003). During no-go trials, immediately following cue onset, participants are still able to retrieve task rules and even though these are never applied, their retrieval should interfere with subsequent performance. In other words eliminating response selection during no-go trials should eliminate residual switch cost but not RISC. This is contrary to what was observed.
One natural explanation for this paradox is that in this paradigm participants did not in fact retrieve task rules during preparation. If only processes occurring at the level of response selection contributed to the switch cost then only residual switch cost should be present. Accordingly, eliminating such processes during no-go trials would be expected to eliminate this switch cost, as observed. Consistent with this explanation, in a further exploration of the effects observed by Schuch and Koch (
2003), Kleinsorge and Gajewski (
2004) demonstrated that as they increased participants' motivation to prepare, RISC effects were increased across trial types. As such the degree to which participants prepare, correlates with the magnitude of subsequent RISC effect. This explanation however is insufficient to account for Schuch and Koch’s findings because they, unlike Kleinsorge and Gajewski (
2004, “Neutral Context” condition), observed RISC following go trials (e.g., Experiment 1a), suggesting that participants engaged in task preparation. If participants were simply not motivated to engage in task preparation then RISC should have been absent following go trials
as well as following no-go trials.
Therefore Schuch and Koch’s (
2003) findings present an interesting puzzle. Why would participants appear to engage in task preparation only following go trials? One intriguing possibility is that processing of no-go trials has the effect of interfering with task preparation. Such interference would be expected to selectively eliminate switch cost following no-go stimuli, as observed. No-go trials may, for instance, trigger a global inhibition signal (Aron & Verbruggen,
2008; De Jong, Coles & Logan,
1995), which has been hypothesized to occur whenever responses need to be stopped quickly (Aron,
2007; Aron & Poldrack,
2006) such as during no-go trials. Beyond its effects on motor responses, such a signal may be expected to also inhibit task rules that are held in working memory. Another possibility is that no-go stimuli reset, rather than inhibit, working memory. For instance, no-go stimuli occur infrequently and are unrelated, in stimuli and responses, to the primary task. As such they may trigger an increase in vigilance that has the side effect of clearing working memory. Indeed such “workspace flushing” has been proposed as a core mechanism that prevents control systems from perseverating on irrelevant response patterns (Gilbert & Shallice,
2002; Logan & Gordon,
2001). More simply, no-go stimuli may also be perceived as a third task, in which case all subsequent go trials would be equivalent to switch trials.
1 Switch cost would not be expected in this case, though responses should be slowed relative to repetition trials. This is consistent with a general slowing of responses following no-go trials relative to go trials (e.g., Kleinsorge & Gajewski,
2004; Schuch & Koch,
2003).
These possibilities clearly demonstrate that no-go trials might have actively interfered with task preparation in Schuch and Koch’s (
2003) study, which would account for the absence of RISC following these trials but not following go trials. If so, then removing no-go trials should reveal effects of task preparation. Such a result would not only help explain Schuch and Koch’s paradoxical result, but would also imply that processes occurring at the level of response selection can modulate the efficacy of task preparation. Namely, effects of task preparation can go unnoticed. This hypothesis was the object of the present study.
General discussion
The aim of this study was to evaluate whether no-go stimuli interfere with task preparation, thereby modulating preparatory effects on switch cost during subsequent trials. We first showed that the absence of switch costs in a no-go paradigm is not an artifact of the use of blocked CSIs, and then demonstrated that both switch cost and RISC were significant when no-go stimuli were replaced with cue-only trials. This finding provides evidence that, independent of their effects on response selection, no-go stimuli can interfere with the effects of task preparation. In the remainder of this discussion, we consider the mechanisms by which this interference may occur and the implications of our findings for studying task preparation.
No-go stimuli and mechanisms of interference
Two plausible mechanisms by which no-go stimuli may interfere with task preparation are inhibition and task reset. The first possibility is that no-go stimuli are interpreted as a stop signal. If so they may be expected to elicit inhibition of the motor response as well as inhibition of recently retrieved task rules. Such a global, non-specific inhibition mechanism has been proposed to operate when responses need to be stopped quickly (Aron & Verbruggen,
2008; Coxon, Stinear & Byblow,
2007; De Jong, Coles & Logan,
1995; Verbruggen, Liefooghe & Vandierendonck,
2006), as may be expected with the occurrence of no-go stimuli. One assumption of this hypothesis is that inhibition at the level of motor responses can spread to representations of task rules. This assumption is not implausible. Spread of inhibition is well documented as a contributing factor in retrieval induced forgetting (Anderson & Neely,
1996; Norman, Newman & Detre,
2007) and in transfer effects within negative priming (Tipper & Driver,
1988; Tipper, MacQueen & Brehaut,
1988). A similar cascade may be initiated by no-go stimuli, with inhibition spreading from motor to rule representations. However, motor inhibition does not automatically spread to rule representations and so it may not necessarily be a factor in the current study. Logan (
1983,
1985) showed that stop-signal generated inhibition of motor responses had no effect on the recall of word-pairs associated with that response, counter to the idea of spreading inhibition. The variables that determine whether inhibition spread occurs, and thus whether it was present in our experiment, are unclear. However, one prediction of the inhibition hypothesis is that if task rules were inhibited then the activation level of the task-rule representation should be below baseline. If so, then responses on a subsequent task repetition should be slowed relative to a task switch. The result would be an inverse switch cost following no-go trials, akin to effects of backwards inhibition (Koch, Gade & Philipp,
2004; Mayr & Keele,
2000). As there was no such slowing following no-go trials (Experiment 1), we suggest that task rules were not inhibited following no-go trials in the current study.
An alternate mechanism by which no-go stimuli could interfere with task preparation is by clearing rule representations that were active in working memory. This could occur because no-go stimuli are unrelated to the task itself and/or because they require an interruption in task flow. Conceptually, this idea is similar to the “flushing” of response counters proposed by Logan and Gordon (
2001) in their model of executive control, which was suggested to occur after each response in order to keep working memory open to new inputs and thus prevent perseveration (see also Gilbert & Shallice,
2002). No-go stimuli may have a similar effect in our experiment, but “flushing” all of working memory rather than just response representations. In this sense no-go stimuli may be perceived as a third task that, because it shares no response mappings with others, produces no interference and thus no inhibition (Botvinick, Braver, Barch, Carter & Cohen,
2001; Gade & Koch,
2005). However, it does disrupt processing by clearing working memory. Another potentially related effect of disruption in task switching occurs whenever performance is briefly stopped. Comparing the first trial of a task-switching block to subsequent trials in that block reveals a relative slowing, referred to as the “restart cost” (Allport & Wylie,
2000; Gopher, Armony & Greenshpan,
2000). This pause effect may be similar to that of no-go stimuli in that, following both, activation of the relevant task representation must be rebuilt (Altmann & Gray,
2002; Poljac, Koch & Bekkering,
2009). Consistent with this interpretation, a general slowing has been observed following no-go trials relative to following go trials (Kleinsorge & Gajewski,
2004; Koch & Philipp,
2005; Schuch & Koch,
2003). In the current study such slowing was observed as well, though primarily following repetition trials (Experiment 1). Based on these observations we suggest that the most likely effect of no-go trials is to clear working memory, and as a consequence they eliminate effects of prior task preparation.
Implications for task preparation
Insofar as they may obscure the effects of task preparation, no-go stimuli have direct impact on interpretations regarding the occurrence and scope of preparatory processes. For instance, in Experiment 1, an absence of switch cost following no-go stimuli may be interpreted as evidence that task preparation contributed little if anything to switch cost beyond the effects of response selection. However, such a conclusion would be incorrect because, in Experiment 2, we saw that preparation for cue-only trials clearly modulated subsequent switch cost.
Of perhaps greater significance is whether our result can inform the scope of preparatory processes. In particular, the notion that task preparation involves retrieval of task rules (Allport & Wylie,
1999; de Jong,
2000; Gilbert & Shallice,
2002; Mayr & Kliegl,
2000; Meiran,
1996; Monsell,
1996; Rubinstein, Meyer & Evans,
2001; Sohn & Anderson,
2001; Yeung & Monsell,
2003) has been questioned by the observation that RISC effects may reflect priming of visual encoding of the cue, rather than active preparation of the cued task (Logan & Bundesen,
2003,
2004; Logan & Schneider,
2006). Thus, far we have been agnostic regarding these possibilities, however, our results may be interpreted as evidence for the task-rule retrieval account. Cue priming effects should be most pronounced during short CSI sequences because these trials present the greatest challenge to cue encoding. Accordingly, the greatest switch cost may be expected following cue-only trials in 350/350 CSI-sequences. However, in Experiment 2 (Fig.
3) we found that switch cost following the 1,250/350 CSI-sequence cue-only trials (113 ms) was about 50% greater than following the 350/350 CSI-sequence cue-only trials (75 ms). Though this difference was only a trend,
t(20) = 1.18,
p = 0.13, it may suggest that participants engaged in more than cue encoding during the cue-only trials.
Of course this conclusion is based on the assumption that cues were completely encoded within the 350 ms CSI. This assumption may not be appropriate for abstract cues such as those used in our study (Logan & Bundesen,
2004). Stronger evidence would be to show a reliable switch cost, whilst controlling for cue priming. Brass and von Cramon (
2004) did exactly that by using multiple cues in a cue-only paradigm similar to that used in the current study. Even with cue priming effects eliminated, they also found a significant switch cost following cue only trials. We also have evaluated switch cost in this paradigm with cue repetitions removed (Lenartowicz & Cohen,
2006) and found switch cost to be significant following cue only trials. Perhaps even more convincing is the finding of Kleinsorge and Gajewski (
2004) who demonstrated measurable switch cost and RISC following no-go trials when the probability of a subsequent task repetition was increased to 80%. Apparently, with sufficient motivation, the interfering effects of no-go stimuli can be overcome, implying an active process such as task-set retrieval rather than passive visual priming of cue encoding. Considering these findings, we suggest that switch cost following cue-only trials may be particularly sensitive to the effects of task preparation, and thus task-rule retrieval when it exists.