## Abstract

Behavioral studies show that subjects respond more slowly to stimuli to which they previously stopped. This response slowing could be explained by “automatic inhibition” (i.e., the reinstantiation of motor suppression when a stimulus retrieves a stop association). Here we tested this using TMS. In Experiment 1, participants were trained to go or no-go to stimuli. Then, in a test phase, we compared the corticospinal excitability for go stimuli that were previously associated with stopping (no-go_then_go) with go stimuli that were previously associated with going (go_then_go). Corticospinal excitability was reduced for no-go_then_go compared with go_then_go stimuli at a mere 100 msec poststimulus. Although these results fit with automatic inhibition, there was, surprisingly, no suppression for no-go_then_no-go stimuli, although this should occur. We speculated that automatic inhibition lies within a continuum between effortful top–down response inhibition and no inhibition at all. When the need for executive control and active response suppression disappears, so does the manifestation of automatic inhibition. Therefore, it should emerge during go/no-go learning and disappear as performance asymptotes. Consistent with this idea, in Experiment 2, we demonstrated reduced corticospinal excitability for no-go versus go trials most prominently in the midphase of training but it wears off as performance asymptotes. We thus provide neurophysiological evidence for an inhibition mechanism that is automatically reinstantiated when a stimulus retrieves a learned stopping episode, but only in an executive context in which active suppression is required. This demonstrates that automatic and top–down inhibition jointly contribute to goal-directed behavior.

## INTRODUCTION

Stopping an ongoing or initiated action is an important part of everyday behavior. Most stopping research has focused on outright stopping, which is triggered by an external signal and is regulated by an “executive control” network including the right inferior frontal gyrus (rIFG), the pre-SMA, the basal ganglia, and the primary motor cortex (for reviews, see Aron, 2011; Boehler, Appelbaum, Krebs, Hopf, & Woldorff, 2010; Chambers, Garavan, & Bellgrove, 2009; Verbruggen & Logan, 2008b). However, recent evidence suggests that stopping can also be triggered in an unintentional (bottom–up) fashion by the retrieval of previously acquired stimulus–stop associations, leading to the slowing down of responses—so called “automatic inhibition” (Verbruggen & Logan, 2008a). This phenomenon could be practically useful. For example, it has been shown that when pictures of alcoholic drinks were paired with stopping, there was a subsequent reduction in alcohol-related consumption (Houben, Nederkoorn, Wiers, & Jansen, 2011). It is therefore important to better understand the neurophysiological mechanisms underlying automatic inhibition. Here we do so by using TMS to probe the corticospinal excitability of response representations during task performance.

The original study by Verbruggen and Logan (2008a) hypothesized that automatic inhibition develops when stimuli are consistently paired with stopping during a training phase. To measure the effects of automatic inhibition, they compared the RTs for stimuli that were no-go in the training phase but became go stimuli in a test phase (i.e., no-go_then_go, the “inconsistent condition”) with go stimuli that stayed go (i.e., go_then_go, the “consistent condition”). In a series of experiments, they found that responding to go stimuli in the test phase was slower for the inconsistent items compared with the consistent items. On the basis of these findings, Verbruggen and Logan (2008a) proposed that response inhibition could be triggered automatically by the retrieval of stimulus–stop associations after practice.

A follow-up neuroimaging study with a similar paradigm demonstrated that inconsistent go items (that were associated with stopping in the training phase) activated the rIFG (Lenartowicz, Verbruggen, Logan, & Poldrack, 2011). As the rIFG is a key component of neural circuitry for inhibitory control (e.g., Aron, Behrens, Smith, Frank, & Poldrack, 2007; Chikazoe, Konishi, Asari, Jimura, & Miyashita, 2007), the finding suggests that the retrieval of stimulus–stop associations (i.e., automatic inhibition) has its counterpart in a reinstantiation of inhibitory control at the neural level. However, because activation of the rIFG has been associated with other functions, such as attentional orienting, reversal learning, conflict, and working memory (e.g., Dodds, Morein-Zamir, & Robbins, 2011; Levy & Wagner, 2011; Hampshire, Chamberlain, Monti, Duncan, & Owen, 2010), the activation of rIFG in this paradigm cannot be taken as proof that the retrieval of stimulus–stop associations necessarily comes along with inhibitory control over the motor system.

Here, we tested whether this response slowing was indeed because of automatic inhibition in the motor system triggered by the reversal of the stimulus go/no-go mapping. We used the same go/no-go paradigm as Verbruggen and Logan (2008a, Experiment 1) along with TMS to probe corticospinal excitability. On each trial in the training phase, participants made a semantic judgment about a word (e.g., living vs. nonliving). The category determined whether the participant should go or stop (e.g., living = go, and nonliving = no-go). In two groups of participants, we manipulated the consistency of the stimulus go/no-go mapping across a training phase and a test phase. For the consistent group, the go/no-go mapping in the test phase corresponded to the go/no-go mapping of the training phase. For the inconsistent group, the mapping was reversed; that is, the stimuli that were associated with stopping (no-go) in the training phase required a go response in the test phase, and vice versa. In the test phase, we applied TMS to the contralateral motor cortex of the responding hand (only one hand was used to respond on go trials) and measured the motor-evoked potentials (MEPs) at precisely 100, 150, 200, and 250 msec after the SOA. The magnetic pulse leads to action potentials in pyramidal cells in the primary motor cortex, and these cells project to the spinal cord and ultimately to the muscles of (in this case) the hand. The MEP—a measure of corticospinal excitability—reflects the net influence of cortical and subcortical motor regions on the specific response channel that is measured at the hand with EMG.

On the basis of the previous behavioral results (Verbruggen & Logan, 2008a), the automatic inhibition hypothesis makes two clear predictions for the MEP pattern. First, there will be reduced corticomotor excitability soon after the presentation of stimuli that were previously associated with stopping. Specifically, we expected (a) in the consistent condition, lower MEPs for no-go (i.e., no-go_then_no-go) trials than go (i.e., go_then_go) trials and (b) in the inconsistent condition, lower MEPs for go (i.e., no-go_then_go) trials than no-go (i.e., go_then_no-go) trials. In other words, we predicted an interaction between trial type and mapping. Furthermore, we hypothesized that the MEP reduction, because of “automatic inhibition,” would occur at the early preresponse initiation SOAs of 100 or 150 msec (cf. Jahfari, Stinear, Claffey, Verbruggen, & Aron, 2010). This prediction was motivated by a recent EEG study that observed a reduction in the stimulus-evoked potential very early in time (61–104 msec poststimulus) for no-go stimuli at the end of a go/no-go training, which is possibly mediated by a fast-acting inhibitory mechanism (Manuel, Grivel, Bernasconi, Murray, & Spierer, 2010).

Second, at the later SOAs of 200 or 250 msec, we predicted a main effect of mapping and an interaction between trial type and mapping. That is, MEPs will be larger for go trials than for no-go trials, but this difference should be more pronounced in the consistent group than in the inconsistent group. In the consistent group, the MEPs for go items should start to increase at these later SOAs as motor facilitation starts to build up (cf. Jahfari et al., 2010). However, in the inconsistent condition, this buildup will be delayed because of the time required to recover from putative automatically triggered inhibition, which could account for the slower RTs.

Alternatives to the “automatic inhibition” account are mentioned in the General Discussion. However, to anticipate the outcome of this experiment, the findings were consistent with a modified version of the “automatic inhibition” hypothesis, according to which stimulus–stop associations do suppress motor output, but only in settings in which active suppression is occasionally required.

## EXPERIMENT 1

### Methods

#### Apparatus and Stimuli

The same apparatus was used as in Experiment 1. Only the smaller/larger categorization task was used in this experiment. Each word was presented 10 times in 10 blocks.

#### Procedure

All participants underwent 10 blocks of categorization task. The category–go/no-go mapping (e.g., smaller = go, and larger = no-go) was counterbalanced across participants. The trial progression was the same as the testing phase in Experiment 1, except for the TMS pulse time. During each trial, one TMS pulse was delivered at one of the time points (i.e., −200 [baseline], 100, 200, and 300 msec) after the onset of the stimulus word. In each block, TMS was delivered on 12 trials at −200 msec (i.e., before the word onset) and 20 trials at each of the other time points. The ordering of different TMS time points was randomized for each block and each participant.

#### TMS Procedure

The same TMS procedure was used as in Experiment 1.

### Results

#### Behavioral Performance

Over the course of 10 blocks of training, RTs on go trials and probabilities of responding on no-go trials [p(respond|no-go)] decreased (see Figure 4A and B), suggesting that participants learned the stimulus–go and stimulus–stop associations. There was a significant main effect of Training Block, F(9, 117) = 28.28 for RT; F(9, 117) = 12.57 for p(respond|no-go; both ps < .0001).

Figure 4.

Behavioral performance and MEP data in Experiment 2. (A) RTs for correct go trials. (B) Probability of responding during no-go trials or p(respond|no-go). Both behavioral measures indicated sufficient learning of stimulus go/no-go mapping in 10 blocks. (C) Normalized MEP amplitude at 100 msec poststimulus during the early (Blocks 1–3), mid (Blocks 4–7), and late (Blocks 8–10) learning phases for go and no-go trials. The quadratic interaction between go versus no-go and learning phase was significant (p < .03), suggesting that the suppression effect was most prominent during the midphase of learning.

Figure 4.

Behavioral performance and MEP data in Experiment 2. (A) RTs for correct go trials. (B) Probability of responding during no-go trials or p(respond|no-go). Both behavioral measures indicated sufficient learning of stimulus go/no-go mapping in 10 blocks. (C) Normalized MEP amplitude at 100 msec poststimulus during the early (Blocks 1–3), mid (Blocks 4–7), and late (Blocks 8–10) learning phases for go and no-go trials. The quadratic interaction between go versus no-go and learning phase was significant (p < .03), suggesting that the suppression effect was most prominent during the midphase of learning.

#### MEPs

To examine the MEPs at different learning phases, we binned the MEP data into three phases (early: Blocks 1–3, middle: Blocks 4–7, and late: Blocks 8–10) and conducted a 3 Learning Phases (early, middle, and late) × 2 Trial Types (go, no-go) ANOVA separately for each TMS time (100, 200, 300 msec). On the basis of the findings of Experiment 1, we were especially interested in the earliest TMS time, that is, 100 msec. The ANOVA at 100 msec revealed a significant main effect of Trial Type, F(1, 13) = 7.93, p < .02, and a significant quadratic interaction between Learning Phase and Trial Type, F(1, 13) = 5.93, p < .03. The main effect of Phase was not significant, F(2, 26) = 1.7, p > .1. The quadratic interaction shows that the MEPs for no-go trials were significantly lower than go trials especially during the midphase of learning, but this effect was not evident yet during the early phase and was wearing off during the late learning phase (see Figure 4C).

The ANOVAs at 200 and 300 msec both revealed significant main effects of trial type, F(1, 13) = 5.15, 16.17, respectively, all ps < .05. No other main effects or interactions were significant, all ps > .06.

Finally, to provide a full view of the MEP data and ensure that we did not miss other interesting effects, we conducted a full ANOVA of 3 Learning Phases (early, middle, and late) × 2 Trial Types (go, no-go) × 3 TMS Times (100, 200, 300 msec). This ANOVA revealed a significant main effect of Learning Phase, F(2, 26) = 3.7, p = .04, trial type, F(1, 13) = 15.48, p = .002, and TMS Times, F(2, 26) = 15.78, p < .001. No other effects were significant, all ps > .6. However, the quadratic interaction between Trial Type and Learning Phase was marginally significant, F(1, 13) = 1.9, p = .14, which was related to the significant interaction at 100 msec reported above.

To verify that the FDI muscle was equally at rest before the TMS pulse in each condition, an ANOVA with 3 Learning Phases (early, middle, and late learning phase) × 2 Trial Types (go, no-go) × 3 TMS Times (100, 200, 300 msec) was performed. No significant main effects or interactions were found (all ps > .5).

In Experiment 2, we observed significantly reduced MEPs for no-go trials than go trials at merely 100 msec poststimulus, most prominently in the midphase of learning. Thus, in the early stage, the suppression was not well developed, likely due to not enough learning yet, and at the late stage, the effect was wearing off. This pattern of results is consistent with our earlier conjecture that automatic inhibition develops during learning when executive control is required but is not applied after substantial practice when the performance reaches asymptote and the need for active suppression of inappropriate responses disappears (i.e., the late learning stage of Experiment 2 and the consistent condition at the test phase of Experiment 1). The results here complement those of Experiment 1 and demonstrate the development of automatic inhibition in the no-go trials during learning.

## GENERAL DISCUSSION

The automatic inhibition hypothesis proposes that motor suppression is triggered by the retrieval of previously acquired stimulus–stop associations, leading to the slowing down of responses (Verbruggen & Logan, 2008a). Consistent with this, we observed, in Experiment 1, that MEPs at a mere 100 msec poststimulus were reduced for no-go_then_go trials compared with go_then_no-go trials in the inconsistent condition (Figure 3A). Thus, a history of stopping (no-go training with particular stimuli) can lead to subsequent quick motor suppression even if the participant has to now respond to those stimuli.

However, we did not see reliable motor suppression in the consistent mapping condition although automatic inhibition predicts lower MEPs for no-go_then_no-go than for go_then_go trials. To reconcile these findings, we supposed that automatic inhibition does develop in the consistent condition during learning but wears off as the performance asymptotes (or when less inhibitory control is needed as the suppression of incorrectly triggered motor responses is no longer required). Consistent with this new hypothesis, in Experiment 2, we demonstrated that MEPs for no-go trials during training were significantly reduced compared with go trials at a mere 100 msec poststimulus, but only during the midpoint of training (Blocks 4–7) around the time when performance started to asymptote. Taken together, the results from both experiments support a “revised automatic inhibition” hypothesis: Automatic inhibition does indeed involve motor suppression, but it only emerges in situations in which active suppression of motor output is required occasionally. Thus, “automatic inhibition” may be in the middle of a continuum between effortful top–down response inhibition and no inhibition at all. When the need for overt response inhibition (an “executive setting”) disappears altogether, so does the manifestation of automatic inhibition. We suppose this is because when there is no executive setting, the “stopping network” is not active and so the “no-go tag” that was created during training cannot trigger the inhibitory system.

An alternative possibility that could explain the slowing down for inconsistent items in the test phase is a “pure conflict” account. On this view, there is conflict between the learned (but no longer relevant) plan to stop (no-go) and the new plan to go, and this leads to response slowing. When such conflict is detected, all motor output might be suppressed until the conflict is resolved (see, e.g., Frank, Samanta, Moustafa, & Sherman, 2007). This “mismatch” account predicts reduced MEPs (beneath baseline) for both go and no-go trials in the inconsistent condition, likely because of the caution for making correct responses. However, it does not predict a difference between go and no-go trials in the inconsistent condition because conflict should occur for both trial types. Although our data did show reduced MEPs (lower than baseline 1.0) for both go and no-go trials in the inconsistent condition, which is consistent with the first prediction of the conflict account, there was also a reliable difference in MEPs for no-go_then_go than go_then_no-go items at a mere 100 msec poststimulus. This latter result is not at all predicted by the pure conflict account. Another problem for the pure conflict account is that there was suppression at a mere 100 msec poststimulus. This seems too quick for a conflict process that occurs based on a mismatch of plans, which must require stimulus categorization and rule retrieval. For example, a recent ERP study (Randall & Smith, 2011) examined “conflict” in terms of the mismatch between cued action and target action (e.g., subjects were cued to go by left/right arrow and were presented with a no-go target) and found a difference in the N2 component. This suggested that a conflict process takes place around 200 msec after target onset (by our visual inspection, unlikely to be earlier than 150 msec). Assuming that after practice, the living/nonliving categorization became as easy as arrow left/right discrimination, we can assume that the conflict process would not have an effect on MEPs until 200 msec. Therefore, if the pure conflict account is true, any effect in the MEPs would not be detected until 200 msec or later.

By contrast, an effect within 100 msec does seem feasible for an automatic reinstantiation of stopping because it does not need stimulus categorization. Consistent with this, a recent EEG study showed that, after a 30-min go/no-go training phase, there was modulation of evoked potentials to no-go stimuli (but not go stimuli) 61–104 msec after SOA (Manuel et al., 2010). The authors proposed that this early effect was consistent with a low-level form of inhibitory control. Thus, the currently observed motor suppression at a mere 100 msec after the stimulus is a plausible time frame for automatic inhibition to be in effect.

Yet, while a pure conflict account does not entirely explain our results, “conflict” between going and stopping does seem important for the expression of motor suppression, leading to a “revised automatic inhibition hypothesis. Our data argue that automatic inhibition emerges during training of go/no-go (in the midpoint, when “conflict” between go and no-go is putatively maximal), but it “disappears” later. We suppose that sometime toward the end of 10 blocks of training (Experiment 2) or 12 blocks of training (Experiment 1), the participant has had so much practice at the go/no-go discrimination that it becomes a matter of facilitating the response if it is go and not doing anything if it is no-go. Thus, a response tendency is no longer triggered on no-go trials after sufficient practice, so the need for both bottom–up and top–down inhibition in the task disappears altogether. When there is no longer any need for inhibition, retrieval of stimulus–stop associations may no longer need to suppress the motor output. As we pointed out above, this is consistent with a recent finding by Verbruggen and Logan (2009) showing that the word “stop” did not have an effect on going when stopping was completely “irrelevant” to the task. However, in the inconsistent mapping of Experiment 1, there was a response mapping reversal, consequently inhibition was occasionally required because responses previously associated with going were triggered incorrectly. Similarly, we assume that in the middle of training phase of Experiment 2, response inhibition was still required because of an incorrect tendency to respond on some trials. We propose that especially under such circumstances, “automatic inhibition” manifests itself.

The results of this study suggest that responses can be suppressed automatically by the retrieval of stimulus–stop associations. We suspect that the same cortical network mediating inhibitory control during deliberate stopping also implements automatic inhibition. Evidence for this was provided by Lenartowicz et al. (2011) whose fMRI study demonstrated that the rIFG was activated for stimuli that had previously been associated with stopping. Although the rIFG is part of a top–down inhibitory control network for deliberate stopping (e.g., Aron et al., 2007), activation of this region cannot be taken to unequivocally prove that inhibitory control is being generated during other conditions. However, our current findings with TMS provide strongly congruent evidence in favor of the idea that the rIFG activation in Lenartowicz et al. (2011) did reflect automatic inhibition. A related line of research by van Gaal and colleagues (van Gaal, Ridderinkhof, Scholte, & Lamme, 2010; van Gaal, Ridderinkhof, van den Wildenberg, & Lamme, 2009) has demonstrated that inhibition can be triggered by subliminally presented no-go primes. These primes slowed responding and activated the top–down inhibitory network (the rIFG and the presupplementary area). Furthermore, the stronger the activation in these regions, the greater the RT slowing because of the no-go primes (van Gaal et al., 2010). Thus, taken together, these results suggest that the inhibition network can be activated in different ways: In a top–down fashion when an external, conscious signal is presented, or in a bottom–up fashion when stimulus–stop associations are retrieved, or when no-go signals are presented subliminally.

One note about our study is that we used the go/no-go paradigm instead of the modified version of the stop-signal paradigm in Lenartowicz et al. (2011). It is still a debatable question whether one can equate the inferred mechanisms involved in these two paradigms. Whereas some studies have shown that motor inhibition in the two paradigms engaged an overlapping neural network (e.g., Levy & Wagner, 2011; Chikazoe, et al., 2007), others have argued for dissociable circuits (e.g., Swick, Ashley, & Turken, 2011). Nonetheless, according to Verbruggen and Logan (2008a), automatic inhibition can be retrieved as long as there is consistent stimulus–stop mapping during training. Thus, our go/no-go paradigm provided a strong case for testing this hypothesis. Furthermore, in the modified stop-signal task, the number of critical trials for testing hypothesis is less than 50% of total trials, which makes the testing multiple TMS SOAs impractical.

How could bottom–up forms of inhibition contribute to goal-directed behavior in real life? Developing such forms of inhibition might be useful to modify motivated behaviors, as studies have shown that pairing “no-go” to a rewarding stimulus leads to a diminution of the motivated behavior (e.g., Houben, 2011; Wiers, Rinck, Kordts, Houben, & Strack, 2010; Veling & Aarts, 2009). In a behavioral study, Houben et al. (2011) instructed two groups of heavy drinking students to perform a task in which pictures of beer were paired with either go and no-go, respectively, and investigated how subsequent drinking behavior in these groups was modulated by the go/no-go training. They found that, a week later, participants in the beer/no-go condition reported a decrease in their weekly alcohol consumption compared with their preexperiment consumption. These results suggest that learned stimulus–stop (e.g., beer/no-go) associations can effectively influence goal-directed behavior. Future therapeutic programs could leverage this approach for treating maladaptive behaviors in clinical disorders, which are characterized by poor self-control over urges and compulsions, such as obsessive-compulsive disorders, Tourette's syndrome, or substance abuse. However, much work is needed to establish the neurocognitive mechanisms by which such therapeutic effects are mediated. Whereas in our study we provide evidence for automatic inhibition at the motor level in a semantic categorization task, it is uncertain whether this mechanism could apply in such circumstance as reduced drinking.

In conclusion, the current study shows that the retrieval of previously acquired stimulus–stop associations can automatically lead to motor suppression. Our MEP evidence of inhibition corroborates and extends previous findings in the literature, suggesting that response inhibition is controlled by both a top–down and a bottom–up mechanism. Finally, clinical treatments for impulse control disorders could consider taking the approach of building up automatic inhibition to counteract specific motivated behaviors or overlearned motor tendencies.

## Acknowledgments

This study was funded by the National Institutes of Health, National Institute on Drug Abuse (grant ROI-DA026452 to A. R. A.).

Reprint requests should be sent to Yu-Chin Chiu, 9500 Gilman Drive, #0109, University of California, San Diego, La Jolla, CA 92093, or via e-mail: chiu.yuchin@gmail.com.

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