Neuropsychologia 50 (2012) 2860–2868
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Repetition priming in the stop signal task: The electrophysiology of sequential effects of stopping J.F.E. Oldenburg a,n, C. Roger a, S. Assecondi b, F. Verbruggen c, W. Fias a a
Department of Experimental Psychology, Ghent University, Belgium Center for Mind and Brain Sciences (CIMeC), University of Trento, Italy c Psychology, College of Life and Environmental Sciences, University of Exeter, United kingdom b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 9 December 2011 Received in revised form 22 July 2012 Accepted 16 August 2012 Available online 24 August 2012
Inhibition of a response affects the processing of subsequent stimuli. When a response has to be made to a stimulus to which a response was previously inhibited, response time increases. In this study, we investigated the neurophysiological underpinnings of this repetition priming phenomenon. We aimed at distinguishing between two possible mechanisms. Firstly, it could be that slowing after a successful inhibition trial originates at the response execution level and is due to the reactivation of the system responsible for motor inhibition interfering with execution of the go response. The second possibility is that interference occurs at the more abstract level of conflicting action goals or plans (i.e. ‘‘stop’’ and ‘‘go’’) that are activated prior to response execution. We analyzed activity over primary motor cortices and the parietal cortex in a stop signal task. Stimulus repetition led to a decrease in activity over primary motor cortices but irrespective of history of stopping. Stopping on the previous trial did affect the stimulus-locked parietal P300 only on repetition of the stimulus, mimicking the behavioral pattern. Furthermore, the P300 was lateralized and affected by both stimulus onset and response time, suggesting that the interference caused by inhibition priming is situated between stimulus perception and response execution. Taken together, these findings show that the prolonged response times to a stimulus that was previously successfully inhibited to, do not originate from reactivated suppression of motor output, but are caused by interference between a stop and a go goal in parietal cortex that hampers translation from stimulus to response. & 2012 Elsevier Ltd. All rights reserved.
Keywords: Inhibition P300 After-effects Motor cortex Goal priming Parietal cortex
1. Introduction A capacity central to human behavior is the ability to regulate interactions with the environment. This so-called cognitive control lets us plan our actions in accordance with the requirements of our ever-changing surroundings and is therefore a key aspect underlying our self-propelled way of living. One important component of cognitive control is response inhibition. It refers to the ability to suppress a planned or ongoing action in response to changes in the environment or internal state. One of the most commonly used paradigms to investigate inhibition is the stop signal paradigm (Lappin & Eriksen, 1966; Logan & Cowan, 1984). In the typical version of this paradigm, participants have to respond to visual stimuli in a simple choice reaction time task. On a Go trial, participants respond to the visual stimulus via a button press. On a Stop trial, an auditory signal (i.e. the stop
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signal) is presented shortly after the visual stimulus and tells participants to withhold their response. Inhibiting a response can lead to sequential effects in the form of interference during processing of a subsequent trial as well. This is reflected in the fact that reaction times on Go trials are longer if they follow an inhibition trial than if they follow a Go trial (Emeric et al., 2007; Kramer, Humphrey, Larish, Logan, & Strayer, 1992; Rieger & Gauggel, 1999; Schachar et al., 2004; Verbruggen & Logan, 2008; Verbruggen, Logan, Liefooghe, & Vandierendonck, 2008). To explore the origins of this slowing, Verbruggen, Logan, and Stevens (2008; experiment 1B) devised an experiment in which subjects were presented with stimuli to which they had to respond unless a stop signal was presented. Trials were analyzed sequentially, making it possible to separate stimulus repetition from stimulus alternation trials. The authors found that reaction times were slowed on Go trials that followed a trial in which the response was successfully stopped, but only when the stimulus was repeated. In a subsequent experiment (Verbruggen et al., 2008; experiment 2), the authors showed that repetition of a response alone did not induce this repetition effect and that it was thus driven specifically by the repetition of
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the stimulus. This repetition priming effect suggests that the repeated stimulus reactivates the processes that were involved in stopping the response on the previous trial. Despite the progress that has been made in understanding the after-effects of inhibition, the level of explanation of the repetition priming effect remains quite general and lacks functional detail. Standard reaction time analyses do not specify at what processing level reactivation causes interference. Therefore, in the present study we used event-related potentials (ERPs) to elucidate where in the processing chain the locus of interference resides that is responsible for the delay in reaction times associated with the after-effects of inhibition. Two possible hypotheses can be considered, depending on the degree of involvement of the motor system in the priming-induced reactivation, namely the response suppression hypothesis and the goal competition hypothesis. The response suppression hypothesis holds that that repeated presentation of a stimulus that has just been associated with a suppressed motor response automatically triggers the inhibition network, leading to suppression of motor activity at the primary motor cortex (M1) and/or premotor cortex. It has been suggested that inhibition of a response depends on a fronto-striatal network in which inferior frontal cortex (IFC), pre-supplementary motor cortex or both, block thalamocortical output, thereby cancelling execution of the response (Aron & Poldrack, 2006; Chambers et al., 2006). Some studies suggest that after sufficient practice, stimuli that were repeatedly paired with stopping activated the default inhibition network (Chiu, Aron, & Verbruggen, 2012; Lenartowicz, Verbruggen, Logan, & Poldrack, 2011). Immediate after-effects may also be caused by such ‘automatic’ reactivation of the motor inhibition network (see also Enticott, Bradshaw, Bellgrove, Upton, & Ogloff, 2009). In order to assess the validity of the response suppression hypothesis for after-effects, we will measure activity over lateral motor cortices (i.e. at electrode positions C3 and C4, reflecting activity of primary motor cortex (M1) and adjacent premotor cortex). It has been shown that inhibitory processes on stopsignal trials affect motor execution by actively suppressing accumulated response activation, likely due to increased intracortical inhibition at M1 (Band & van Boxtel, 1999; van den Wildenberg et al., 2009). As a measure of the activity of the (pre)motor cortex, we use an ERP known as the ‘‘activation– inhibition’’ pattern (Burle, Vidal, Tandonnet, & Hasbroucq, 2004; Vidal, Grapperon, Bonnet, & Hasbroucq, 2003). Vidal and Colleagues, 2003 showed that, preceding response execution, activity over the motor cortex contralateral to the responding hand is characterized by a negative wave, whilst a positive wave develops over the cortex ipsilateral to the responding hand, evidencing suppression of the incorrect response in favor of execution of the correct response. This activation–inhibition pattern can be used to separate specific motor activity corresponding to the hand that is actually involved in the execution of the response from the hand that is not involved in the response. Thus, using the activation–inhibition pattern as a marker, we will examine whether and how primed inhibition after a Stop trial might interfere at lateral motor cortex to actively suppress accumulated response activation. Alternatively, following the goal competition hypothesis, the after-effects of inhibition do not emanate from the motor level, but are the consequence of competition at an earlier, more abstract level of processing, involving the cognitive goal or action plan triggered by a stimulus (i.e. to respond or not). Planning and execution phases of intended movement have been shown to be able to function independently. In a study by Desmurget et al. (2009), premotor and parietal areas were electrically stimulated in patients. Whereas stimulation of the parietal region evoked the intention to perform a movement without producing actual
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execution of the movement, stimulation of the premotor region did lead to execution of movement. In the latter case however, the patients stated they had not consciously intended, or even executed the movement. From this, the authors concluded that conscious intention and motor awareness stem from parietal cortex and suggest that they precede overt movement. This suggestion of temporal separation of parietal and premotor contribution to movement is supported by the results of Taubert and colleagues (Taubert et al., 2010) who found that applying a virtual lesion using transcranial magnetic stimulation (TMS) to parietal cortex, but not premotor cortex, affects earlier stages of a grasping movement. Conversely, a virtual lesion to the premotor, but not parietal cortex affects later stages of the movement. Thus, it is conceivable that inhibition after-effects arise due to interference at the level of these early movement plans, without actually affecting motor execution itself (see also Enticott et al., 2009): slowing after a Stop trial could be due to two incongruent response plans (in this case ‘‘stop’’ and ‘‘go’’) becoming activated by the presentation of the same stimulus, and it is only when the incorrect stop plan is subdued in favor of the currently correct go plan that the response can be executed. This selection of the correct plan will increase processing time between stimulus perception and response execution, resulting in the observed reaction time delay. To evaluate the validity of the goal competition hypothesis, we will examine the parietal P300 (or P3b; (Squires, Squires, & Hillyard, 1975), a positive component that is maximal at parietal electrodes around 300 ms after stimulus onset (note that this parietal P300 is different from the P3a, typically recorded over frontal and central electrodes and reflecting attentional processes as they occur in for instance the oddball task (Comerchero & Polich, 1999). The parietal P300 has since long been associated with stimulus evaluation time (e.g. Kutas, Mccarthy, & Donchin, 1977). Originally, the term stimulus evaluation was used to indicate the time it took to perceptually process a specific stimulus. However, the P300 has not only been found to be affected by stimulus discrimination, but also by response selection demands (Falkenstein, Hohnsbein, & Hoormann, 1994; Leuthold & Sommer, 1998), suggesting that the process of stimulus evaluation also entails the selection or representation of its associated motor plan. This suggestion is strengthened by the widely held view that parietal cortex, identified as the main source of the P300 (e.g. Bledowski et al., 2004), integrates sensory and motor information (Andersen & Buneo, 2002; Bunge, Hazeltine, Scanlon, Rosen, & Gabrieli, 2002; Coulthard, Nachev, & Husain, 2008; Deiber, Ibanez, Sadato, & Hallett, 1996; Deiber et al. 1991; Desmurget & Sirigu, 2009; Grafton, Fagg, & Arbib, 1998; Jackson, Jackson, & Roberts, 1999; Jeannerod, Arbib, Rizzolatti, & Sakata, 1995; Kalaska & Crammond, 1995; Rushworth, Ellison, & Walsh, 2001; Rushworth, Krams, & Passingham, 2001; Sakata, Taira, Murata, & Mine, 1995; Taira, Mine, Georgopoulos, Murata, & Sakata, 1990). This allows it to evoke potential action plans on the basis of learned stimulus-response associations and that competition between those plans can lead to prolonged processing in parietal cortex (Bunge et al., 2002; Coulthard et al., 2008). Indeed, some research using the Go NoGo paradigm has suggested that the modulation of the P300 found in the NoGo condition in relation to the Go condition might be due to conflict or competition between the expected ‘‘Go’’ response and the currently relevant ‘‘NoGo’’ response (Smith, Johnstone, & Barry, 2007; Smith, Smith, Provost, & Heathcote, 2010). Thus, if slowing after a Stop trial would be due to the automatically retrieved response plan, namely stopping (associated with the stimulus in the previous trial), that competes with the current response plan, namely going (the current task goal), the evidence described above suggests that this interference would lead to prolonged processing in parietal cortex and a modulation of
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the P300. This entails that the correct response is executed only when this competition is resolved, implying that activity related to the response execution itself, as measured over the motor cortices, is not affected by the interference, only that the response is delayed in time.
2. Current study Our main goal is to pinpoint the locus of interference of repetition priming in the stop-signal paradigm by means of ERPs. We will use a paradigm similar to that of Verbruggen et al. (2008; experiment 1B). Of all possible types of transitions (trial N and trial N 1 can be either go trial correctly responded, go trial incorrectly responded, stop trial successfully stopped and stop trial unsuccessfully stopped; moreover every trial N can repeat or alternate the stimulus N 1), we are specifically interested in the following possible trial types: A Go trial following a Stop trial in which inhibition was successful and a Go trial following a correct Go trial, both with either repeating or alternating stimuli. We expect that, like in the study of Verbruggen et al. (2008; experiment 1B), participants will be slower to respond after a Stop trial, but only when the stimulus is repeated. If this slowing is due to the reactivation of the stop goal interfering with the current go goal at the level prior to response execution (goal competition hypothesis), we expect a prolongation of stimulus-response translation in parietal cortex and a difference between conditions in the stimulus locked parietal P300 wave (e.g. Verleger, Jaskowski, & Wascher, 2005). Conversely, if reactivation of the stop goal leads to automatic response suppression (response suppression hypothesis), we expect this to be reflected by a modulation of response locked activity over (pre)motor cortex (e.g. van den Wildenberg et al., 2009).
procedure to obtain a probability of responding of .50. Each time a subject responded to a stimulus although a stop signal was presented, SSD decreased by 50 ms. Conversely, each time a subject successfully stopped the response when a signal was presented, SSD increased by 50 ms. Before the start of the experiment, participants received a written description of the experimental procedure. The instructions reported therein told participants to respond ‘‘as quickly as possible’’ upon stimulus presentation but to try to withhold the response in case a stop signal was presented. It was emphasized not to wait for the stop signal to occur and that experimental characteristics would make it difficult to withhold the response on some trials and easy on others. After instructions, they received a practice phase of 65 trials. 3.4. Psychophysiological recording and EEG data analysis Participants were seated in a dimly lit, electrically shielded room. EEG recordings were collected using an electrode cap fitted with electrodes at 31 sites of the international 10–20 system. Electrodes were referenced to the mean of all electrodes and grounded halfway between Fpz and Fz. Horizontal eye movements (EOG) were recorded with tin electrodes placed 1 cm lateral to the outer canthi of the eyes. Vertical EOG was measured with tin electrodes placed 1 cm above and below the left eye. Electrode impedance was kept below 3 kO. The EEG signals were amplified (REFA-64 amplifier, TMS International, Netherlands) and digitized at 512 Hz. Electrophysiological data analysis was performed using BrainAnalyser& (Brain Products, Munich). All electrode recordings were re-referenced to the left and the right mastoids. Data were filtered (high pass¼ 0.16 Hz, low pass¼30 Hz, notch¼50 Hz). Eye movement artefacts were corrected by an ocular correction ICA transform procedure implemented by BrainAnalyser. Artefacts were screened with automatic detection methods. In particular, signals with amplitude higher than 200 mV or lower than 200 mV were excluded from the analysis. 3.5. Laplacian transform We improved the spatial and temporal resolution of the EEG data (Law, Rohrbaugh, Adams, & Eckardt, 1993) by applying the current source density (Laplacian) transform (Perrin, Pernier, Bertrand, & Echallier, 1989). We first interpolated the signal with the spherical spline interpolation procedure, using 31 for the spline. The interpolation was computed with a maximum of 151 for the Legendre polynomial. Then, the second derivatives in two dimensions of space were computed on the monopolar averages of each participant.
3. Methods
3.6. Single-trial analysis
3.1. Subjects
We adopted a single-trial approach by representing the data in ‘‘ERP-image’’ plots implemented by the EEGLAB software (Delorme & Makeig, 2004). This method of visualization allows for clear characterization of single-trial dynamics in the amplitudes and latencies of evoked responses in relation to behavioral variables (Jung et al., 2001). In the ERP-images, the trial traces were stacked vertically and sorted based on reaction time for each subject. The individual ERP-images were then averaged to provide a ‘‘grand-averaged’’ ERP-image (see (Burle, Roger, Allain, Vidal, & Hasbroucq, 2008) for further detail). As recommended by Jung et al. (2001), the sorted trials were smoothed with a moving window in order to increase response signal-to-noise ratio. The size used for this window was 10% of trials in a condition. These sorted trial visualizations were used for display purposes only and were not further addressed with formal statistical analyses.
Participants were 15 adults (13 women, M age¼ 19,5 years) with normal or corrected to normal vision. They were screened for apparent neurological impairment by means of a questionnaire. All participants were right handed and received payment for participation in the experiment. 3.2. Apparatus and stimuli The experiment was run on a Pentium 4 PC running STOP-IT (Verbruggen et al., 2008). The visual stimuli were presented on a 17-inch CRT monitor. The target stimuli in the go task were a circle (diameter 1 cm) or a square (1 1 cm). The fixation cross (‘þ ’) and target stimuli were presented in white on a black background. Behavioural responses were collected by means of response buttons held by participants in their left and right hand. Occasionally, stop signals were presented acoustically as a clearly audible tone (750 Hz, 75 ms) through small speakers next to the monitor. 3.3. Design and procedure The experimental phase consisted of 15 blocks of 65 trials. Excluding the first trial of each block, half of the trials were stimulus-repetition trials and half were stimulus-alternation trials. The squares and circles were equally divided over trials. Each trial began with the presentation of a fixation cross, which was replaced after 250 ms by the target stimulus. The target remained on the screen for 1250 ms and was then followed by an inter-trial interval of 750 ms. On the majority of trials participants had to respond by means of a button press on a hand-held response key with their left or right thumb. Each target was associated with only one response during the entire experiment, which means that stimulus repetitions automatically encompassed response repetitions. The presentation of a square required a response with the left hand, whereas the presentation of a circle always required a response with the right hand. On 25% of the repetition and alternation trials, a stop signal was presented. The stop signal was initially presented 250 ms after the presentation of the target stimulus. The stop-signal delay (SSD) was then further adjusted dynamically according to a tracking
3.7. Statistical analyses Analyses of reaction times and electrophysiological results were done only on Go trials that were responded to correctly. Three trial-types were distinguished, as a function of the trial that preceded the Go trial: (1) Go trials following a Go trial that was responded to correctly (Gocorr Gocorr), (2) Go trials following a trial in which participants had to inhibit but actually wrongfully responded (Stoperr Gocorr), (3) Go trials following trials in which participants correctly inhibited (Stopcorr Gocorr). These could occur for cases where the target stimulus was repeated or alternated. Consequently, the data were analyzed by means of Condition (Gocorr Gocorr, Stoperr Gocorr, Stopcorr Gocorr) Repetition (repetition, alternation) repeated measures analyses of variance ANOVA. For EEG analyses, we did not take into account activity in the Stoperr Gocorr condition because post-error processing influences the activity in this condition, hampering interpretation of the inhibition after-effects. In the absence of clear a priori predictions about the timing of the effects, time windows for analyses of components were chosen based on visual inspection of grand average waveforms. To reduce the risk of artificially inflating significance, which is inherent to the exploratory approach of selecting by inspection (Good & Hardin, 2003), we made sure not to delineate our selected interval based on the time windows where the conditions differed maximally. Rather, we used the selection to determine on- and/or offset of the component of interest. Following our predictions regarding location, we analysed electrodes covering motor and parietal areas. To analyze motor activity at C3 and C4
J.F.E. Oldenburg et al. / Neuropsychologia 50 (2012) 2860–2868 electrodes, the time window was set between 200 and 0 preceding response execution. For the P300 at P3 and P4, the time window was set between 200 and 800 ms post-stimulus. The areas under the curve in these time windows were subjected to a Condition (Gocorr Gocorr, Stopcorr Gocorr) Repetition (repetition, alternation) repeated measures ANOVAs.
4. Results 4.1. Behavioural results Analyses were performed on reaction times of the correct Go trials. Reaction times more than 1250 ms and less than 100 ms were excluded. This trimming procedure resulted in a data loss of 0.2%. Mean SSD was 233 ms with a proportion of correct stops of .49. Of the included trials, 49.02% were repetition trials. A Condition (Gocorr Gocorr, Stoperr Gocorr, Stopcorr Gocorr) Repetition (repetition, alternation) repeated measures analysis of variance (ANOVA) on reaction times showed a main effect of Condition (F(2,28)¼16.51, po.001), repetition (F(1,14)¼ 5.68, po.05) and a significant Condition Repetition interaction effect (F(2,28)¼ 18,43, po.001) (Fig. 1). Post hoc (Bonferroni corrected) comparisons showed that, in the alternation condition, there were no differences in reaction times between Gocorr Gocorr, Stoperr Gocorr, and Stopcorr Gocorr trials. In the repetition condition however, reaction times were longer in Stopcorr Gocorr trials (570 ms) than in Stoperr Gocorr trials (533 ms) (po .05). Stoperr Gocorr reaction times in turn were longer than reaction times in Gocorr Gocorr trials (492 ms) (po.001). Reaction times in the Gocorr Gocorr condition were longer for alternation (516 ms) than for repetition trials (po.001). In Stoperr Gocorr condition reaction times were greater for repetition than for alternation trials (511 ms) p o.05). Lastly, Stopcorr Gocorr condition reaction times were also greater for repetition than for alternation trials (536 ms) (p o.001). Thus, there is slowing after an inhibition trial, but only on repetition if the stimulus that has been stopped to, is repeated. This pattern of results replicates previous findings (Verbruggen et al., 2008), and the stimulus specificity of suggests that the after-effects of stopping were not due to nonstationarity of RTs (Nelson, Boucher, Logan, Palmeri, & Schall, 2010). A Condition (Gocorr Goerr, Stoperr Goerr, Stopcorr Goerr) Repetition (repetition, alternation) repeated measures ANOVA on error percentages showed no significant differences (Fig. 1). 4.2. Electrophysiological results Our main goal was to see whether the locus of interference related to the primed inhibition was to be found in motor execution due to automatic activation of an inhibitory system or at a stage prior to response execution due to competition between the incongruent stop and go goals. Crucially, we were looking for a pattern of electrophysiological activity that would mimic that of the behavioural results in the sense that it would show an interaction between the repetition of a stimulus and inhibition to that stimulus on the previous trial. To this end, we defined the primary motor cortex area contralateral to the responding hand,
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the primary motor cortex area ipsilateral to the responding hand, and ipsi- and contralateral parietal cortices as regions of interest (Willemssen, Hoormann, Hohnsbein, & Falkenstein, 2004). 4.2.1. Motor components Based on visual inspection of the grand average waveform (see Fig. 2A), we performed the analysis on the components over lateral motor cortices within a 200 to 0 ms interval, using a time window of 400 to 200 ms prior to the response as baseline. We performed a Hemisphere (contralateral vs. ipsilateral) Repetition (Repetition vs. Alternation) Condition (Stopcorr Gocorr, Gocorr Gocorr) repeated measures ANOVA on the response locked area under the curve of the activation–inhibition pattern measured over the motor cortices. We found a main effect of Hemisphere (ipsilateral vs. contralateral) (F(1,14)¼15.60, po.005). In addition, we found a main effect of stimulus repetition for both contralateral (F(1,14)¼ 9.82, po.05) and ipsilateral (F(1,14)¼5.09, po.05) cortices. Specifically, the components measured over the contralateral primary motor cortices were more negative for alternation than for repetition trials and components measured over the ipsilateral primary motor cortices were more positive for alternation than for repetition trials. This means that repeating the stimulus (which was always paired with the same response) affected motor execution. If the slowing in the behavioural results would be due to stimulus-associated inhibition directly affecting the motor command, we would expect this effect of repeating the stimulus to be modulated by a history of stopping on that stimulus. Yet, there was no Repetition Condition interaction effect (F(1,14)o1). So, even though there seems to be a difference in motor execution between repeating and alternating responses, there is no indication that successful stopping on the previous trial influences motor commands in any way. 4.2.2. Parietal components The second possible explanation for slower reaction times after inhibition is that the after-effects of stopping are due to interference prior to execution, brought about by competing goals on a more abstract level. To see whether components over parietal cortices were modulated, we examined the area under the curve for stimulus locked components in the parietal cortices ipsilateral and contralateral to the responding hand (Fig. 2B). The pre-stimulus onset activity at 250 ms in stimulus locked components is due to the presentation of a fixation cross prior to the stimulus. Based on visual inspection of the grand average waveform, a time window between 200 and 800 ms was selected with a baseline interval 400 to 200 ms. Within this time window, we performed a Hemisphere (contralateral vs. ipsilateral) Repetition (Repetition vs. Alternation) Condition (Stopcorr Gocorr, Gocorr Gocorr) repeated measures ANOVA on the stimulus locked area under the curve of the P300. We found an effect of Condition (F(1,14)¼ 9.2, po0.01) and a Condition Repetition interaction effect showing that the difference between Gocorr Gocorr and Stopcorr Gocorr was larger for a repetition than for an alternation (F(1,14)¼11.2, po0.005). This pattern replicates the interaction effect between Repetition and Condition found in the reaction time data and supports the goal competition hypothesis.
Fig. 1. Mean reaction times (left panel) and error percentages per condition.
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Fig. 2. (A.) Grand average component waves over primary motor cortices after Laplacian transformation (ordinate in mV/cm2) time-locked to the response at time zero for correct responses. Contralateral (grey) and ipsilateral (black) primary motor cortices for Gocorr Gocorr and Stopcorr Gocorr conditions are shown in left and right panel, respectively. For left responses, C4 was contralateral, and C3 ipsilateral, and vice versa for right responses. Dashed lines represent alternation (alt) trials and full lines represent repetition (rep) trials. (B.) Grand average stimulus locked components at ipsilateral (left) and contralateral parietal cortices. Dashed lines represent alternation (alt) trials and full lines represent repetition (rep) trials. Grey and black lines represent Stopcorr Gocorr and Gocorr Gocorr trials, respectively. Time zero corresponds to stimulus presentation. Pre-stimulus onset activity at 200 ms is due to the presentation of a fixation cross prior to the stimulus.
To further specify the process that is affected by the interference, we wanted to dissociate whether the goal competition would be at an abstract level entirely unrelated to response demands, or that it incorporated response information to some extent in the guise of specific response representations or motor plans, as suggested by previous work (e.g. Bunge et al., 2002; Verleger et al., 2005). A powerful indication of the involvement of response demands in the P300 is that it is modulated by the hand selected to make the response (Wascher, Reinhard, Wauschkuhn, & Verleger, 1999; Wauschkuhn, Wascher, & Verleger, 1997; Willemssen et al., 2004). To investigate this, we performed a repeated measures ANOVA on Go–Go trials only, with Hemisphere (ipsilateral vs. contralateral) as a factor. We found a main effect of Hemisphere (F(1,14)¼20.97, p o.005), indicating that the area under the curve of the P300 component was significantly smaller at the cortex ipsilateral to the responding hand (214.0) than at the cortex contralateral to the responding hand (3524.8) (see Fig. 3). This finding is in line with previous evidence suggesting that parietal cortex activates specific lateralized response codes on the basis of stimulus information (Wascher et al., 1999; Wauschkuhn et al., 1997; Willemssen et al., 2004). To be sure that the effect of hemisphere was really related to the executed response, we separated the Go–Go data between left and right responses and examined activity at left-hemispheric P3 and right-hemispheric P4 electrodes separately. We performed a Response side (left vs. right) Electrode (P3 vs. P4) repeated measures ANOVA on the component area under the curve. As expected, we found a significant Response Side Electrode interaction effect (F(1,14¼11.2, po.005; see Fig. 4). Activity at P3 was greater for a right response (2142) than for a left response (2203). Conversely, activity at P4 was greater for a left response (2863) than for a right response (1393). In Fig. 4, the topographies clearly show that activity over the parietal cortex is lateralized so that activity is more positive
Fig. 3. Grand average waveforms at parietal cortices ipsi- and contralateral to the responding hand, time-locked to the stimulus at time zero. Pre-stimulus onset activity at 200 ms is due to the presentation of a fixation cross prior to the stimulus.
in the area contralateral to the responding hand than in the area ipsilateral to the responding hand. This shows that already at a parietal level, a dissociation is made between response sides. However, if the P300 is truly a component reflecting a mechanism active during preparation towards the response based on stimulus information, we expect this lateralization to be time locked to the response as well as to the stimulus (Verleger et al., 2005). Therefore, we analysed the component response locked as well. As expected, we found the same results as with the stimulus locked analyses. Namely, there is a main difference between ipsilateral and contralateral parietal cortices (F(1,14)¼15.1, po.005). Again, the area under the curve of the component was significantly smaller at the cortex ipsilateral to the responding hand (661.2) than at the cortex
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Fig. 4. Top panel: Topographies of Laplacian estimated activities between 300 and 500 ms post-stimulus related to left and right responses represented on head models seen from the back. Positive activations are shown in red and negative activations in blue. Here, it can be seen that the positive parietal activations (shown by an arrow) shift in time from a bilateral to a contralateral orientation. Bottom panel: Grand average stimulus locked component waves at P3 (left) and P4 electrodes separated for left and right responses.
contralateral to the responding hand (1396.4). This is supported by the single-trial ERP images (see Fig. 5), which clearly show that in all conditions the P300 component is time locked to the stimulus and then extends until the response is executed.
5. Discussion The aim of the current study was to explore the mechanisms underlying post-inhibition slowing on stimulus-repetition trials. We tested two hypotheses: The repeated stimulus could automatically reactivate the inhibitory network, thereby suppressing motor activation directly (response suppression hypothesis); alternatively, the stop goal previously associated with the stimulus could compete with the current correct go goal causing interference at a more abstract level prior to response execution, thus hampering stimulus-response translation and delaying reaction times (goal competition hypothesis). 5.1. The response suppression hypothesis To see whether response suppression due to automatic reactivation of the inhibitory network would underlie the slowing found in reaction times, we examined components measured over C3 and C4, reflecting motor activity at M1 and/or premotor cortex. It was reasoned that automatic activation of the inhibition network triggered by the stop goal attached to the repeated stimulus would suppress motor activation directly, delaying the response. Previous research indeed has suggested that response inhibition affects motor execution by actively suppressing accumulated response activation. For instance, van den Wildenberg et al. (2009) applied single pulse transcranial magnetic stimulation over M1 during a stop-signal task, at various time-points after the presentation of the stop signal. They showed that successfully stopped trials coincide with a sharp decrease in the motor evoked potential (a measure of cortico-spinal excitability) and an increased silent period (a measure of activity of
intracortical inhibition). This was taken as evidence that increased intracortical inhibition at M1 underlies an inhibitory mechanism that actively suppresses accumulated response activation in case a response has to be stopped. However, we failed to find that a previous history of stopping associated with the presented stimulus induces stimulus-specific after effects at the level of the lateral motor cortices. Therefore, we presently cannot conclude that presentation of a stimulus that has been associated with stopping on the immediately preceding trial automatically leads to suppression of motor activity. The only effect we found on motor activity is a general repetition effect where repeating a stimulus and its associated response led to a motor component with diminished amplitude over the motor cortex contralateral and ipsilateral to the responding hand. Dirnberger et al. (2003) observed a similar decrease over C3/C4 when a movement was repeated, but only contralaterally. The fact that in the Dirnberger study the movement was predictable, whereas in our choice task this was not the case, may account for the difference. Interestingly, in (Dirnberger, Kunaver, Scholze, Lindinger, & Lang, 2002), where a similar decrease of motor activity was observed, an experimental paradigm without stimuli was used, making stimulus repetition unlikely as an explanation for the diminished motor activity, at the same time emphasizing response repetition to be crucial. As regards the underlying mechanism, an interesting possibility is that this decrease in motor potentials reflects a decrease in evoked cortical response on repetition of a stimulus or response, coined repetition suppression (Desimone, 1996). The accumulation hypothesis of repetition suppression assumes that its pattern of decreased neural activity and its associated decrease in reaction times, is caused by the fact that the accumulation rate of the activation related to the decision process is higher in case of a repetition than it is in case of an alternation, speeding up the decision process (James & Gauthier, 2006). Thus, it could be that a faster decision process accompanied the repetition of a response in the current study and that this was the reason for the decrease in activity and, at least partly, the reason for the fact that in the
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Fig. 5. Grand average trial-by-trial ERP images of activity at contralateral (top) and ipsilateral parietal cortices for both Gocorr Gocorr (left) and Stopcorr Gocorr (right) conditions sorted as a function of reaction time (y-axis), illustrating that in all these cases the parietal activation (in red and shown by the arrow) were initiated at the time of stimulus onset (straight vertical black line) and extend until the time of response (curved vertical black line). Time zero corresponds to stimulus onset. The colour code indicates the intensity of the signal for each trial at each time point (red for positive and blue for negative). Due to the lesser amount of trials in the Stopcorr Gocorr condition, the trials in this condition were smoothed using a smaller window due to a smaller number of trials making the Stopcorr Gocorr more noisy. The different level of smoothing prevents a direct comparison between Stopcorr Gocorr and Gocorr Gocorr.
Go–Go condition, responses on repetition trials were faster than alternation trials. Interestingly, despite such advantages of response repetition, Stopcorr Gocorr repetition trials were still slower than Stopcorr Gocorr alternation trials, suggesting that this beneficial effect of repetition suppression is completely outweighed by the strong effect of the primed inhibition. Future research should be carried out to specifically test this hypothesis. It has to be noted that the observation that stopping history does not modulate activity over motor areas does not mean that other areas commonly active during response inhibition are not reactivated by the stop goal. Based on the results of the current study, it is impossible to exclude that some part of the inhibition network is reactivated by the stimulus that was stopped to on the previous trial. In fact, it has been shown recently that at least the right IFC activation is subject to inhibition priming (Lenartowicz et al., 2011). Moreover, a recent TMS study by Chiu and colleagues (Chiu et al., 2012) has found that automatic inhibition can lead to suppression of response activity. In their study Chiu et al. trained participants to associate specific words with a ‘‘Go’’ or a ‘‘NoGo’’ response in a training phase. Then, in a subsequent test phase, participants were presented with the same stimuli that could
have a stimulus-response mapping that was consistent (eg. Go then Go) or inconsistent (e.g. NoGo then Go) with the mapping in the training phase. It was found that motor activity during the presentation of Go targets that were previously associated with a NoGo response was significantly reduced in relation to NoGo targets that were previously associated with a Go response. From this, the authors concluded that prior association of a specific stimulus with stopping can lead to automatic motor suppression on a subsequent presentation. On first glance, it seems that the results from Lenartowicz et al. (2011) and Chiu et al. (2012) are in direct contradiction to the results found in the current study. However, caution should be exercised when comparing the results of Lenartowicz et al. (2011) and Chiu et al. (2012) with those of the current study, for they have one crucial difference in experimental setup. Whereas in the present study, the pairing between stimulus and the stop goal was changed on a trial-totrial basis the study by Lenartowicz et al. and Chiu et al. followed a design in which participants were first extensively trained, thereby strengthening the association between the stimulus and the stop goal. It has been shown that the neural pathways recruited during the performance of a task can vary, depending
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on the degree of familiarity with task rules or the amount of training with stimulus response associations (Eliassen, Souza, & Sanes, 2003). As such, it could well be that operational differences between the two paradigms lead to differential neural substrates underlying the similar effects in reaction times. Future research will have to precise the neurocognitive differences between trialto-trial and overlearned inhibition priming. 5.2. The goal competition hypothesis Our second hypotheses held that the slowing in response times was not due to automatic activation of the inhibitory system, but to interference caused by competition between the stop and go goals associated with the same stimulus. Based on prior evidence, we expected that this interference would lead to prolonged processing in parietal cortex, which would become apparent through modulation of the parietal P300. This is exactly what we found. Namely, the area under the curve under the P300 was significantly enlarged for the Stopcorr Gocorr condition in comparison to the Gocorr Gocorr condition, but only when the stimulus was repeated. Thus, a previous history of stopping modulates the P300 on encounter of the stimulus. This is consistent with the results of a previous study by Upton and colleagues (Upton, Enticott, Croft, Cooper, & Fitzgerald, 2010) that used a similar design to explore the after-effects of inhibition. Upton and colleagues also found that the P300 was modulated by history of stopping, but only if the stimulus was repeated. To claim that it is competition between the stop and go responses that causes the delay in RT, it is necessary to show that responserelated demands affect the parietal P300. We did so in several ways. First, it was found that the P300 was lateralized, which is in accordance with prior evidence (e.g. Wascher et al., 1999). The fact that activity was always greater over the cortex contralateral to the hand involved in the response is strong evidence for the fact that parietal cortex is able to specifically and directionally code response representations on the basis of visual information, and that these representations bias the P300. Next, this modulation of the P300 occurred both time-locked to the stimulus and to the response, an essential quality of a component involved in both perception of the stimulus and preparation towards the response based on the information carried by that stimulus (Verleger et al., 2005). This idea is further supported by the pattern of activation shown in the ERP images. There, it can be clearly seen that the P300 itself is related to both the stimulus and the response. Namely, the P300 component found in the current study has an onset that is related to the stimulus onset and an offset that is related to reaction time, which is in agreement with the idea that the P300 has the characteristics of a component that reflects the translation from stimulus to response, linking perception to action (Verleger et al., 2005). Based on this evidence, we suggest that the delay in reaction time associated with the after-effects of inhibition are due to prolonged stimulus-response translation in parietal cortex because of competition between the response representations of stopping and going. The current finding of dissociable effects on motor cortex and parietal cortex might be related to prior evidence (Dirnberger et al., 2002, 2003) showing differential contributions of motor and parietal cortices to motor repetition effects, the former being related to changes of the side of movement and the latter to changes of effector (i.e. finger within the hand). How the two types of dissociation relate to each other is a matter for further investigation. All in all, we conclude that inhibition priming, at least on a trial-to-trial basis, does not lead to modulation of activity related to response execution. We therefore cannot conclude that response suppression at the motor level lies at the heart of short-term after-effects of inhibition. Rather, we suggest that
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competition between the automatically activated ‘‘stop’’ goal competes with the current correct ‘‘go’’ goal for translation into an actual response. These results shed new light on inhibition priming and provide interesting insights into the mechanisms underlying the parietal P300.
Acknowledgements ˜ ez Castellar for help with data collection We thank Elena Nu´n and Anke Marit Albers for proofreading the manuscript and providing helpful comments. This work was supported by the Ghent University Multidisciplinary Research Partnership ‘‘The integrative neuroscience of behavioral control’’ and by grant P6/ 29 from the Interuniversitary Attraction Poles program of the Belgian federal government.
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